Remote Sensing

15 pages, 420 KiB

Open AccessTechnical Note

Two-Dimensional Direction Finding for L-Shaped Coprime Array via Minimization of the Ratio of the Nuclear Norm and the Frobenius Norm

by Lang Zhou, Kun Ye and Xuebo Zhang

Remote Sens. 2024, 16(18), 3543; https://doi.org/10.3390/rs16183543 - 23 Sep 2024

Viewed by 695

Abstract

More recently, the ability of the coprime array to yield large array apertures and high degrees of freedom in comparison with the uniform linear array (ULA) has drawn an enormous amount of attention. In light of this, we propose a low-rank matrix completion [...] Read more.

More recently, the ability of the coprime array to yield large array apertures and high degrees of freedom in comparison with the uniform linear array (ULA) has drawn an enormous amount of attention. In light of this, we propose a low-rank matrix completion algorithm via minimization of the ratio of the nuclear norm and the Frobenius norm (N/F) to solve the two-dimensional (2D) direction finding problem for the L-shaped coprime array (LsCA). Specifically, we first interpolate the virtual co-array signal related to the cross-correlation matrix (CCM) and utilize the interpolated virtual signal for Toeplitz matrix reconstruction. Then, the N/F method is employed to perform low-rank matrix completion on the reconstructed matrix. Finally, exploiting the conjugate symmetry characteristics of the completed matrix, we further develop a direction-finding algorithm that enables 2D angle estimation. Remarkably, the 2D angles are able to be automatically paired by the proposed algorithm. Numerical simulation findings demonstrate that the proposed N/F algorithm generates excellent angular resolution and computational complexity. Furthermore, this algorithm yields better estimation accuracy compared to the competing algorithms. Full article

(This article belongs to the Special Issue Advanced Array Signal Processing for Target Imaging and Detection (Second Edition))

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15 pages, 963 KiB

Open AccessArticle

Automatic Radar Intra-Pulse Signal Modulation Classification Using the Supervised Contrastive Learning

by Jingjing Cai, Yicheng Guo and Xianghai Cao

Remote Sens. 2024, 16(18), 3542; https://doi.org/10.3390/rs16183542 - 23 Sep 2024

Viewed by 688

Abstract

The modulation classification technology for radar intra-pulse signals is important in the electronic countermeasures field. As the high quality labeled radar signals are difficult to be captured in the real applications, the signal modulation classification base on the limited number of labeled samples [...] Read more.

The modulation classification technology for radar intra-pulse signals is important in the electronic countermeasures field. As the high quality labeled radar signals are difficult to be captured in the real applications, the signal modulation classification base on the limited number of labeled samples is playing a more and more important role. To relieve the requirement of the labeled samples, many self-supervised learning (SeSL) models exist. However, as they cannot fully explore the information of the labeled samples and rely significantly on the unlabeled samples, highly time-consuming processing of the pseudo-labels of the unlabeled samples is caused. To solve these problems, a supervised learning (SL) model, using the contrastive learning (CL) method (SL-CL), is proposed in this paper, which achieves a high classification accuracy, even adopting limited number of labeled training samples. The SL-CL model uses a two-stage training structure, in which the CL method is used in the first stage to effectively capture the features of samples, then the multilayer perceptron is applied in the second stage for the classification. Especially, the supervised contrastive loss is constructed to fully exploring the label information, which efficiently increases the classification accuracy. In the experiments, the SL-CL outperforms the comparison models in the situation of limited number of labeled samples available, which reaches 94% classification accuracy using 50 samples per class at 5 dB SNR. Full article

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17 pages, 1797 KiB

Open AccessArticle

Central Difference Variational Filtering Based on Conjugate Gradient Method for Distributed Imaging Application

by Wen Ye, Fubo Zhang and Hongmei Chen

Remote Sens. 2024, 16(18), 3541; https://doi.org/10.3390/rs16183541 - 23 Sep 2024

Viewed by 519

Abstract

The airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units (IMUs), a data-processing computer, and a Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing applications, such as array SAR and multi-node imaging [...] Read more.

The airborne distributed position and orientation system (ADPOS), which integrates multi-inertia measurement units (IMUs), a data-processing computer, and a Global Navigation Satellite System (GNSS), serves as a key sensor in new higher-resolution airborne remote sensing applications, such as array SAR and multi-node imaging loads. ADPOS can provide reliable, high-precision and high-frequency spatio-temporal reference information to realize multinode motion compensation with the various nonlinear filter estimation methods such as Central Difference Kalman Filtering (CDKF), and modified CDKF. Although these known nonlinear models demonstrate good performance, their noise estimation performance with its linear minimum variance estimation criterion is limited for ADPOS. For this reason, in this paper, Central Difference Variational Filtering (CDVF) based on the variational optimization process is presented. This method adopts the conjugate gradient algorithm to enhance the estimation performance for mean correction in the filtering update stage. On one hand, the proposed method achieves adaptability by estimating noise covariance through the variational optimization method. On the other hand, robustness is implemented under the minimum variance estimation criterion based on the conjugate gradient algorithm to suppress measurement noise. We conducted a real ADPOS flight test, and the experimental results show that the accuracy of the slave motion parameters has significantly improved compared to the current CDKF. Moreover, the compensation performance shows a clear enhancement. Full article

(This article belongs to the Special Issue Array and Signal Processing for Radar)

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19 pages, 6879 KiB

Open AccessArticle

Design and Analysis of a Moon-Based Earth-Radiation Measurement System

by Shuqi Li, Zhitao Luo, Yanfeng Liu, Wei Fang, Yuwei Wang, Ruidong Jia, Duo Wu, Baoqi Song, Xiaolong Yi and Xin Ye

Remote Sens. 2024, 16(18), 3540; https://doi.org/10.3390/rs16183540 - 23 Sep 2024

Cited by 1 | Viewed by 518

Abstract

This research project envisions using a lunar observation platform to measure the full-wave (0.2~100 μm) and shortwave (0.2~4.3 μm) radiation of the Earth, achieving an accurate estimation of the overall radiation budget of the Earth. Based on the lunar platform, the system analyzes [...] Read more.

This research project envisions using a lunar observation platform to measure the full-wave (0.2~100 μm) and shortwave (0.2~4.3 μm) radiation of the Earth, achieving an accurate estimation of the overall radiation budget of the Earth. Based on the lunar platform, the system analyzes Earth’s radiation characteristics and geometric attributes, as well as the sampling properties of observation times. Informed by these analyses, an Earth-facing optical radiation measurement system tailored to these specifications is designed. The optical system adopts an off-axis three-mirror configuration with a secondary image plane, incorporating a field stop at the primary image plane to effectively suppress solar stray light, scattered lunar surface light, and background radiation from the instrument itself, ensuring the satisfactory signal-to-noise ratio, detection sensitivity, and observation duration of the instrument. At the same time, stringent requirements are imposed for the surface treatments of instrument components and temperature control accuracy to further ensure accuracy. Simulation analyses confirm that the design satisfies requirements, achieving a measurement accuracy of better than 1% across the entire optical system. This Moon-based Earth-radiation measurement system, with capabilities for Earth-pointing tracking, radiation energy detection, and stray-light suppression, furnishes a more comprehensive dataset, helping to advance our understanding of the mechanisms driving global climate change Full article

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30 pages, 10615 KiB

Open AccessArticle

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

by Dev Dinesh, Shashi Kumar and Sameer Saran

Remote Sens. 2024, 16(18), 3539; https://doi.org/10.3390/rs16183539 - 23 Sep 2024

Viewed by 1506

Abstract

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools [...] Read more.

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R² of 0.89 and RMSE of 0.050 without considering vegetation effects and an R² of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R² of 0.89 and RMSE of 6.79 without considering vegetation effects and an R² of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar Remote Sensing for Geophysical and Biophysical Parameters Retrieval)

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25 pages, 12343 KiB

Open AccessArticle

Multispectral UAV Image Classification of Jimson Weed (Datura stramonium L.) in Common Bean (Phaseolus vulgaris L.)

by Marlies Lauwers, Benny De Cauwer, David Nuyttens, Wouter H. Maes and Jan G. Pieters

Remote Sens. 2024, 16(18), 3538; https://doi.org/10.3390/rs16183538 - 23 Sep 2024

Viewed by 633

Abstract

Jimson weed (Datura stramonium L.) is a toxic weed that is occasionally found in fields with common bean (Phaseolus vulgaris L.) for the processing industry. Common bean growers are required to manually remove toxic weeds. If toxic weed plants remain, the [...] Read more.

Jimson weed (Datura stramonium L.) is a toxic weed that is occasionally found in fields with common bean (Phaseolus vulgaris L.) for the processing industry. Common bean growers are required to manually remove toxic weeds. If toxic weed plants remain, the standing crop will be rejected. Hence, the implementation of an automatic weed detection system aiding the farmers is badly needed. The overall goal of this study was to investigate if D. stramonium can be located in common bean fields using an unmanned aerial vehicle (UAV)-based ten-band multispectral camera. Therefore four objectives were defined: (I) assessing the spectral discriminative capacity between common bean and D. stramonium by the development and application of logistic regression models; (II) examining the influence of ground sampling distance (GSD) on model performance; and improving model generalization by (III) incorporating the use of vegetation indices and cumulative distribution function (CDF) matching and by (IV) combining spectral data from multiple common bean fields with the use of leave-one-group-out cross-validation (LOGO CV). Logistic regression models were created using data from fields at four different locations in Belgium. Based on the results, it was concluded that common bean and D. stramonium are separable based on multispectral information. A model trained and tested on the data of one location obtained a validation true positive rate and true negative rate of 99% and 95%, respectively. In this study, where D. stramonium had a mean plant size of 0.038 m² (

σ

= 0.020), a GSD of 2.1 cm was found to be appropriate. However, the results proved to be location dependent as the model was not able to reliably distinguish D. stramonium in two other datasets. Finally, the use of a LOGO CV obtained the best results. Although small D. stramonium plants were still systematically overlooked and classified as common bean, the model was capable of detecting large D. stramonium plants on three of the four fields. This study emphasizes the variability in reflectance data among different common bean fields and the importance of an independent dataset to test model generalization. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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24 pages, 1952 KiB

Open AccessArticle

Fast Exclusion Candidate Identification Based on Sparse Estimation for ARAIM Fault Exclusion Process

by Hangtian Qi, Xiaowei Cui and Mingquan Lu

Remote Sens. 2024, 16(18), 3537; https://doi.org/10.3390/rs16183537 - 23 Sep 2024

Viewed by 555

Abstract

Advanced receiver autonomous integrity monitoring (ARAIM) is an integrity technique for a global navigation satellite system (GNSS), centered on the multiple hypothesis solution separation (MHSS) test, which assesses the consistency between a subset and the all-in-view solution. Successful fault exclusion (FE) in ARAIM [...] Read more.

Advanced receiver autonomous integrity monitoring (ARAIM) is an integrity technique for a global navigation satellite system (GNSS), centered on the multiple hypothesis solution separation (MHSS) test, which assesses the consistency between a subset and the all-in-view solution. Successful fault exclusion (FE) in ARAIM relies on identifying exclusion candidates that ensure no faults among the remaining satellites, a process requiring computationally expensive MHSS tests. The existing methods guide exclusion candidate searches based on the size of the normalized solution separation statistics, i.e., the normalized absolute difference between the subset solution and the all-in-view solution. However, in scenarios involving more than one satellite fault, these statistics can become unreliable due to fault diversity and interactions, perhaps misleading the FE process and causing its failure. To overcome this issue, our study proposes employing sparse estimation to simply identify satellite faults in one go, leveraging the sparsity of faulty satellites compared to the total number of observations in civil aviation GNSSs. Unlike the existing methods, which infer the fault likelihood indirectly through solution separation statistics, our approach represents an improvement that directly indicates potential exclusion candidates. Our experiments demonstrate that this method is fast and accurate. As a fundamentally different approach, it serves as a valuable complement or an alternative to the existing methods, enhancing the success and efficiency of the ARAIM FE process. Full article

(This article belongs to the Special Issue Satellite Navigation and Signal Processing (Second Edition))

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21 pages, 9982 KiB

Open AccessArticle

Classification and Mapping of Fuels in Mediterranean Forest Landscapes Using a UAV-LiDAR System and Integration Possibilities with Handheld Mobile Laser Scanner Systems

by Raúl Hoffrén, María Teresa Lamelas and Juan de la Riva

Remote Sens. 2024, 16(18), 3536; https://doi.org/10.3390/rs16183536 - 23 Sep 2024

Viewed by 794

Abstract

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of [...] Read more.

In this study, we evaluated the capability of an unmanned aerial vehicle with a LiDAR sensor (UAV-LiDAR) to classify and map fuel types based on the Prometheus classification in Mediterranean environments. UAV data were collected across 73 forest plots located in NE of Spain. Furthermore, data collected from a handheld mobile laser scanner system (HMLS) in 43 out of the 73 plots were used to assess the extent of improvement in fuel identification resulting from the fusion of UAV and HMLS data. UAV three-dimensional point clouds (average density: 452 points/m²) allowed the generation of LiDAR metrics and indices related to vegetation structure. Additionally, voxels of 5 cm³ derived from HMLS three-dimensional point clouds (average density: 63,148 points/m²) facilitated the calculation of fuel volume at each Prometheus fuel type height stratum (0.60, 2, and 4 m). Two different models based on three machine learning techniques (Random Forest, Linear Support Vector Machine, and Radial Support Vector Machine) were employed to classify the fuel types: one including only UAV variables and the other incorporating HMLS volume data. The most relevant UAV variables introduced into the classification models, according to Dunn’s test, were the 99th and 10th percentile of the vegetation heights, the standard deviation of the heights, the total returns above 4 m, and the LiDAR Height Diversity Index (LHDI). The best classification using only UAV data was achieved with Random Forest (overall accuracy = 81.28%), with confusion mainly found between similar shrub and tree fuel types. The integration of fuel volume from HMLS data yielded a substantial improvement, especially in Random Forest (overall accuracy = 95.05%). The mapping of the UAV model correctly estimated the fuel types in the total area of 55 plots and at least part of the area of 59 plots. These results confirm that UAV-LiDAR systems are valid and operational tools for forest fuel classification and mapping and show how fusion with HMLS data refines the identification of fuel types, contributing to more effective management of forest ecosystems. Full article

(This article belongs to the Section Environmental Remote Sensing)

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18 pages, 6441 KiB

Open AccessArticle

Evaluation of the Operational Global Ocean Wave Forecasting System of China

by Mengmeng Wu, Juanjuan Wang, Qiongqiong Cai, Yi Wang, Jiuke Wang and Hui Wang

Remote Sens. 2024, 16(18), 3535; https://doi.org/10.3390/rs16183535 - 23 Sep 2024

Viewed by 508

Abstract

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform [...] Read more.

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform a detailed evaluation of the system’s forecasting results for 2022, with a focus on China’s offshore and global ocean waters, so as to comprehensively understand the model’s forecasting performance. The study results showed the following: In China’s coastal waters, the model had a high forecasting accuracy for significant wave heights. The model tended to underestimate the significant wave heights in autumn and winter and overestimate them in spring and summer. In addition, the model slightly underestimated low (below 1 m) wave heights, while overestimating them in other ranges. In terms of spatial distribution, negative deviations and high scatter indexes were observed in the forecasting of significant wave heights in semi-enclosed sea areas such as the Bohai Sea, Yellow Sea, and Beibu Gulf, with the largest negative deviation occurring near Liaodong Bay of the Bohai Sea (−0.18 m). There was a slight positive deviation (0.01 m) in the East China Sea, while the South China Sea exhibited a more significant positive deviation (0.17 m). The model showed a trend of underestimation for the forecasting of the mean wave period in China’s coastal waters. In the global oceanic waters, the forecasting results of the model were found to have obvious positive deviations for most regions, with negative deviations mainly occurring on the east coast and in relatively closed basins. There were latitude differences in the forecasting deviations of the model: specifically, the most significant positive deviations occurred in the Southern Ocean, with smaller positive deviations toward the north, while a slight negative deviation was observed in the Arctic waters. Overall, the global wave model has high reliability and can meet the current operational forecasting needs. In the future, the accuracy and performance of ocean wave forecasting can be further improved by adjusting the parameterization scheme, replacing the wind fields with more accurate ones, adopting spherical multiple-cell grids, and data assimilation. Full article

(This article belongs to the Special Issue Validation and Evaluation of Global Ocean Satellite Products (Second Edition))

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17 pages, 11166 KiB

Open AccessArticle

Regional-Scale Image Segmentation of Sandy Beaches in Southeastern Australia

by Suk Yee Yong, Julian O’Grady, Rebecca Gregory and Dylan Lynton

Remote Sens. 2024, 16(18), 3534; https://doi.org/10.3390/rs16183534 - 23 Sep 2024

Viewed by 528

Abstract

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia’s national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping [...] Read more.

Beaches play a crucial role in recreation and ecosystem habitats, and are central to Australia’s national identity. Precise mapping of beach locations is essential for coastal vulnerability and risk assessments. While point locations of over 11,000 beaches are documented from citizen science mapping projects, the full spatial extent and outlines of many Australian beaches remain unmapped. This study leverages deep learning (DL), specifically convolutional neural networks, for binary image segmentation to map beach outlines along the coast of Southeastern Australia. It focuses on Victoria and New South Wales coasts, each approximately 2000 to 2500 km in length. Our methodology includes training and evaluating the model using state-specific datasets, followed by applying the trained model to predict the beach outlines, size, shape, and morphology in both regions. The results demonstrate the model’s ability to generate accurate segmentation and rapid predictions, although it faces challenges such as misclassifying cliffs and sensitivity to fine details. Overall, this research presents a significant advancement in integrating DL with coastal science, providing a scalable solution of citizen science mapping efforts for comprehensive beach mapping to support sustainable coastal management and conservation efforts across Australia. Open access datasets and models are provided to further support beach mapping efforts around Australia. Full article

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Graphical abstract

26 pages, 19057 KiB

Open AccessArticle

Hypergraph Representation Learning for Remote Sensing Image Change Detection

by Zhoujuan Cui, Yueran Zu, Yiping Duan and Xiaoming Tao

Remote Sens. 2024, 16(18), 3533; https://doi.org/10.3390/rs16183533 - 23 Sep 2024

Viewed by 847

Abstract

To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation information, we propose a novel architecture based on hypergraph convolutional neural [...] Read more.

To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation information, we propose a novel architecture based on hypergraph convolutional neural networks. By characterizing superpixel vertices and their high-order correlations, the method implicitly expands the number of labels while assigning adaptive weight parameters to adjacent objects. It not only describes changes in vertex features but also uncovers local and consistent changes within hyperedges. Specifically, a vertex aggregation mechanism based on superpixel segmentation is established, which segments the difference map into superpixels of diverse shapes and boundaries, and extracts their significant statistical features. Subsequently, a dynamic hypergraph structure is constructed, with each superpixel serving as a vertex. Based on the multi-head self-attention mechanism, the connection probability between vertices and hyperedges is calculated through learnable parameters, and the hyperedges are generated through threshold filtering. Moreover, a framework based on hypergraph convolutional neural networks is customized, which models the high-order correlations within the data through the learning optimization of the hypergraph, achieving change detection in remote sensing images. The experimental results demonstrate that the method obtains impressive qualitative and quantitative analysis results on the three remote sensing datasets, thereby verifying its effectiveness in enhancing the robustness and accuracy of change detection. Full article

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21 pages, 22124 KiB

Open AccessArticle

ACDF-YOLO: Attentive and Cross-Differential Fusion Network for Multimodal Remote Sensing Object Detection

by Xuan Fei, Mengyao Guo, Yan Li, Renping Yu and Le Sun

Remote Sens. 2024, 16(18), 3532; https://doi.org/10.3390/rs16183532 - 23 Sep 2024

Viewed by 1111

Abstract

Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting [...] Read more.

Object detection in remote sensing images has received significant attention for a wide range of applications. However, traditional unimodal remote sensing images, whether based on visible light or infrared, have limitations that cannot be ignored. Visible light images are susceptible to ambient lighting conditions, and their detection accuracy can be greatly reduced. Infrared images often lack rich texture information, resulting in a high false-detection rate during target identification and classification. To address these challenges, we propose a novel multimodal fusion network detection model, named ACDF-YOLO, basedon the lightweight and efficient YOLOv5 structure, which aims to amalgamate synergistic data from both visible and infrared imagery, thereby enhancing the efficiency of target identification in remote sensing imagery. Firstly, a novel efficient shuffle attention module is designed to assist in extracting the features of various modalities. Secondly, deeper multimodal information fusion is achieved by introducing a new cross-modal difference module to fuse the features that have been acquired. Finally, we combine the two modules mentioned above in an effective manner to achieve ACDF. The ACDF not only enhances the characterization ability for the fused features but also further refines the capture and reinforcement of important channel features. Experimental validation was performed using several publicly available multimodal real-world and remote sensing datasets. Compared with other advanced unimodal and multimodal methods, ACDF-YOLO separately achieved a 95.87% and 78.10% mAP0.5 on the LLVIP and VEDAI datasets, demonstrating that the deep fusion of different modal information can effectively improve the accuracy of object detection. Full article

(This article belongs to the Special Issue Semantic Segmentation of High-Resolution Remote Sensing Images with Advanced Deep Learning Techniques)

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20 pages, 6402 KiB

Open AccessArticle

PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image Classification Using Multiple Spatial-Spectral Features

by Ningbo Guo, Mingyong Jiang, Decheng Wang, Yutong Jia, Kaitao Li, Yanan Zhang, Mingdong Wang and Jiancheng Luo

Remote Sens. 2024, 16(18), 3531; https://doi.org/10.3390/rs16183531 - 23 Sep 2024

Viewed by 856

Abstract

Hyperspectral image (HSI) shows great potential for application in remote sensing due to its rich spectral information and fine spatial resolution. However, the high dimensionality, nonlinearity, and complex relationship between spectral and spatial features of HSI pose challenges to its accurate classification. Traditional [...] Read more.

Hyperspectral image (HSI) shows great potential for application in remote sensing due to its rich spectral information and fine spatial resolution. However, the high dimensionality, nonlinearity, and complex relationship between spectral and spatial features of HSI pose challenges to its accurate classification. Traditional convolutional neural network (CNN)-based methods suffer from detail loss in feature extraction; Transformer-based methods rely too much on the quantity and quality of HSI; and graph neural network (GNN)-based methods provide a new impetus for HSI classification by virtue of their excellent ability to handle irregular data. To address these challenges and take advantage of GNN, we propose a network of parallel GNNs called PGNN-Net. The network first extracts the key spatial-spectral features of HSI using principal component analysis, followed by preprocessing to obtain two primary features and a normalized adjacency matrix. Then, a parallel architecture is constructed using improved GCN and ChebNet to extract local and global spatial-spectral features, respectively. Finally, the discriminative features obtained through the fusion strategy are input into the classifier to obtain the classification results. In addition, to alleviate the over-fitting problem, the label smoothing technique is embedded in the cross-entropy loss function. The experimental results show that the average overall accuracy obtained by our method on Indian Pines, Kennedy Space Center, Pavia University Scene, and Botswana reaches 97.35%, 99.40%, 99.64%, and 98.46%, respectively, which are better compared to some state-of-the-art methods. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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20 pages, 16168 KiB

Open AccessArticle

Dynamic Monitoring and Analysis of Ecological Environment Quality in Arid and Semi-Arid Areas Based on a Modified Remote Sensing Ecological Index (MRSEI): A Case Study of the Qilian Mountain National Nature Reserve

by Xiuxia Zhang, Xiaoxian Wang, Wangping Li, Xiaodong Wu, Xiaoqiang Cheng, Zhaoye Zhou, Qing Ling, Yadong Liu, Xiaojie Liu, Junming Hao, Tingting Wang, Lingzhi Deng and Lisha Han

Remote Sens. 2024, 16(18), 3530; https://doi.org/10.3390/rs16183530 - 23 Sep 2024

Viewed by 686

Abstract

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region’s socio-economic development. This study investigates the regional characteristics and application requirements [...] Read more.

The ecosystems within the Qilian Mountain National Nature Reserve (QMNNR) and its surrounding areas have been significantly affected by changes in climate and land use, which have, in turn, constrained the region’s socio-economic development. This study investigates the regional characteristics and application requirements of the ecological environment in the arid and semi-arid zones of the reserve. In view of the saturated characteristics of NDVI in the reserve and the high-altitude saline-alkali environmental conditions, this study proposed a Modified Remote Sensing Ecology Index (MRSEI) by introducing the kernel NDVI and comprehensive salinity index (CSI). This approach enhances the applicability of the remote sensing ecological index. The temporal and spatial dynamics of ecological and environmental quality within the QMNNR from 2000 to 2022 were quantitatively assessed using the MRSEI. The effect of land use on ecological quality was quantified by analyzing the MRSEI contribution rate. The findings in this paper indicate that (1) in arid and semi-arid regions, the MRSEI provides a more precise representation of surface ecological environmental quality compared to the remote sensing ecological index (RSEI). The high correlation (R² = 0.908) and significant difference between MRSEI and RSEI demonstrate that MRSEI enhances the accuracy of evaluating ecological environmental quality. The impact of land use on ecological quality was quantitatively assessed by analyzing the contribution rate of the MRSEI. (2) The ecological quality of the QMNNR exhibited an upward trend from 2000 to 2022, with an increase rate of 1.3 × 10⁻³ y⁻¹. The area characterized by improved ecological and environmental quality constitutes approximately 53.68% of the total area. Conversely, the ecological quality of the degraded areas accounts for roughly 28.77%. (3) Among the various land use types, the improvement in ecological environmental quality within the reserve is primarily attributed to the expansion of forest and grassland areas, along with a reduction in unused land. Forest and grassland types account for over 90% of the total area classified with “good” and “excellent” ecological grades, whereas unused land types represent more than 44% of the total area classified with “poor” ecological grades. Overall, this study provides a valuable framework for analyzing ecological and environmental changes in arid and semi-arid regions. Full article

(This article belongs to the Special Issue Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate Ⅱ)

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20 pages, 2723 KiB

Open AccessArticle

Source Range Estimation Using Linear Frequency-Difference Matched Field Processing in a Shallow Water Waveguide

by Penghua Song, Haozhong Wang, Bolin Su, Liang Wang and Wei Gao

Remote Sens. 2024, 16(18), 3529; https://doi.org/10.3390/rs16183529 - 23 Sep 2024

Viewed by 513

Abstract

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To [...] Read more.

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To suppress the sidelobes and improve the method’s robustness, a linear frequency-difference matched field processing (LFDMFP) method for estimating the source range is proposed. A two-neighbor-frequency high-order cross-spectrum between the measurement and the replica of each hydrophone of the vertical line array is first computed. The cost function can then be derived from the dual summation or double integral of the high-order cross-spectrum with respect to the depth of the hydrophones and the candidate sources of the replicas, where the range that corresponds to the minimum is the optimal estimation. Because of the larger modal interference distances, LFDMFP can efficiently provide only one optimal range within the same range search interval rather than some conventional matched field processing. The efficiency of the presented method was verified using simulations and experiments. The LFDMFP unambiguously estimated the source range in two experimental datasets with average relative errors of 2.2 and 1.9%. Full article

(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques)

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22 pages, 6833 KiB

Open AccessArticle

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

by Zhiguo Tai, Xiaokun Su, Wenjuan Shen, Tongyu Wang, Chenfeng Gu, Jiaying He and Chengquan Huang

Remote Sens. 2024, 16(18), 3528; https://doi.org/10.3390/rs16183528 - 23 Sep 2024

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Abstract

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have [...] Read more.

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities. Full article

(This article belongs to the Section Forest Remote Sensing)

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25 pages, 3409 KiB

Open AccessArticle

Weighted Contrastive Prototype Network for Few-Shot Hyperspectral Image Classification with Noisy Labels

by Dan Zhang, Yiyuan Ren, Chun Liu, Zhigang Han and Jiayao Wang

Remote Sens. 2024, 16(18), 3527; https://doi.org/10.3390/rs16183527 - 23 Sep 2024

Viewed by 810

Abstract

Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the potential noisy labels in the [...] Read more.

Few-shot hyperspectral image classification aims to develop the ability of classifying image pixels by using relatively few labeled pixels per class. However, due to the inaccuracy of the localization system and the bias of the ground survey, the potential noisy labels in the training data pose a very significant challenge to few-shot hyperspectral image classification. To solve this problem, this paper proposes a weighted contrastive prototype network (WCPN) for few-shot hyperspectral image classification with noisy labels. WCPN first utilizes a similarity metric to generate the weights of the samples from the same classes, and applies them to calibrate the class prototypes of support and query sets. Then the weighted prototype network will minimize the distance between features and prototypes to train the network. WCPN also incorporates a weighted contrastive regularization function that uses the sample weights as gates to filter the fake positive samples whose labels are incorrect to further improve the discriminative power of the prototypes. We conduct experiments on multiple hyperspectral image datasets with artificially generated noisy labels, and the results show that the WCPN has excellent performance that can sufficiently mitigate the impact of noisy labels. Full article

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20 pages, 37883 KiB

Open AccessArticle

A Novel Framework for Spatiotemporal Susceptibility Prediction of Rainfall-Induced Landslides: A Case Study in Western Pennsylvania

by Jun Xiong, Te Pei and Tong Qiu

Remote Sens. 2024, 16(18), 3526; https://doi.org/10.3390/rs16183526 - 23 Sep 2024

Viewed by 762

Abstract

Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given [...] Read more.

Landslide susceptibility measures the probability of landslides occurring under certain geo-environmental conditions and is essential in landslide hazard assessment. Landslide susceptibility mapping (LSM) using data-driven methods applies statistical models and geospatial data to show the relative propensity of slope failure in a given area. However, due to the rarity of multi-temporal landslide inventory, conventional data-driven LSMs are primarily generated by spatial causative factors, while the temporal factors remain limited. In this study, a spatiotemporal LSM is carried out using machine learning (ML) techniques to assess rainfall-induced landslide susceptibility. To achieve this, two landslide inventories are collected for southwestern Pennsylvania: a spatial inventory and a multi-temporal inventory, with 4543 and 223 historical landslide samples, respectively. The spatial inventory lacks the information to describe landslide temporal distribution; there are insufficient samples in the temporal inventory to represent landslide spatial distribution. A novel paradigm of data augmentation through non-landslide sampling based on domain knowledge is applied to leverage both spatial and temporal information for ML modeling. The results show that the spatiotemporal ML model using the proposed data augmentation predicts well rainfall-induced landslides in space and time across the study area, with a value of 0.86 of the area under the receiver operating characteristic curve (AUC), which makes it an effective tool in rainfall-induced landslide hazard mitigation and forecasting. Full article

(This article belongs to the Special Issue Artificial Intelligence for Slope Stability and Related Infrastructure)

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39 pages, 13148 KiB

Open AccessArticle

Fiducial Reference Measurement for Greenhouse Gases (FRM4GHG)

by Mahesh Kumar Sha, Martine De Mazière, Justus Notholt, Thomas Blumenstock, Pieter Bogaert, Pepijn Cardoen, Huilin Chen, Filip Desmet, Omaira García, David W. T. Griffith, Frank Hase, Pauli Heikkinen, Benedikt Herkommer, Christian Hermans, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Bavo Langerock, Neil A. Macleod, Jamal Makkor, Winfried Markert, Christof Petri, Qiansi Tu, Corinne Vigouroux, Damien Weidmann and Minqiang Zhou Show full author list Hide full author list

Remote Sens. 2024, 16(18), 3525; https://doi.org/10.3390/rs16183525 - 23 Sep 2024

Viewed by 768

Abstract

The Total Carbon Column Observing Network (TCCON) and the Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) are two ground-based networks that provide the retrieved concentrations of up to 30 atmospheric trace gases, using solar absorption spectrometry. [...] Read more.

The Total Carbon Column Observing Network (TCCON) and the Infrared Working Group of the Network for the Detection of Atmospheric Composition Change (NDACC-IRWG) are two ground-based networks that provide the retrieved concentrations of up to 30 atmospheric trace gases, using solar absorption spectrometry. Both networks provide reference measurements for the validation of satellites and models. TCCON concentrates on long-lived greenhouse gases (GHGs) for carbon cycle studies and validation. The number of sites is limited, and the geographical coverage is uneven, covering mainly Europe and the USA. A better distribution of stations is desired to improve the representativeness of the data for various atmospheric conditions and surface conditions and to cover a large latitudinal distribution. The two successive Fiducial Reference Measurements for Greenhouse Gases European Space Agency projects (FRM4GHG and FRM4GHG2) aim at the assessment of several low-cost portable instruments for precise measurements of GHGs to complement the existing ground-based sites. Several types of low spectral resolution Fourier transform infrared (FTIR) spectrometers manufactured by Bruker, namely an EM27/SUN, a Vertex70, a fiber-coupled IRCube, and a Laser Heterodyne spectro-Radiometer (LHR) developed by UK Rutherford Appleton Laboratory are the participating instruments to achieve the Fiducial Reference Measurements (FRMs) status. Intensive side-by-side measurements were performed using all four instruments next to the Bruker IFS 125HR high spectral resolution FTIR, performing measurements in the NIR (TCCON configuration) and MIR (NDACC configuration) spectral range. The remote sensing measurements were complemented by AirCore launches, which provided in situ vertical profiles of target gases traceable to the World Meteorological Organization (WMO) reference scale. The results of the intercomparisons are shown and discussed. Except for the EM27/SUN, all other instruments, including the reference TCCON spectrometer, needed modifications during the campaign period. The EM27/SUN and the Vertex70 provided stable and precise measurements of the target gases during the campaign with quantified small biases. As part of the FRM4GHG project, one EM27/SUN is now used as a travel standard for the verification of column-integrated GHG measurements. The extension of the Vertex70 to the MIR provides the opportunity to retrieve additional concentrations of N₂O, CH₄, HCHO, and OCS. These MIR data products are comparable to the retrieval results from the high-resolution IFS 125HR spectrometer as operated by the NDACC. Our studies show the potential for such types of spectrometers to be used as a travel standard for the MIR species. An enclosure system with a compact solar tracker and meteorological station has been developed to house the low spectral resolution portable FTIR systems for performing solar absorption measurements. This helps the spectrometers to be mobile and enables autonomous operation, which will help to complement the TCCON and NDACC networks by extending the observational capabilities at new sites for the observation of GHGs and additional air quality gases. The development of the retrieval software allows comparable processing of the Vertex70 type of spectra as the EM27/SUN ones, therefore bringing them under the umbrella of the COllaborative Carbon Column Observing Network (COCCON). A self-assessment following the CEOS-FRM Maturity Matrix shows that the COCCON is able to provide GHG data products of FRM quality and can be used for either short-term campaigns or long-term measurements to complement the high-resolution FTIR networks. Full article

(This article belongs to the Special Issue Copernicus Sentinels Missions Calibration, Validation, FRM and Innovation Approaches in Satellite-Data Quality Assessment)

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20 pages, 16568 KiB

Open AccessArticle

Response of Upper Ocean to Parameterized Schemes of Wave Breaking under Typhoon Condition

by Xuhui Cao, Jie Chen, Jian Shi, Jingmin Xia, Wenjing Zhang, Zhenhui Yi, Hanshi Wang, Shaoze Zhang, Jialei Lv, Zeqi Zhao and Qianhui Wang

Remote Sens. 2024, 16(18), 3524; https://doi.org/10.3390/rs16183524 - 23 Sep 2024

Viewed by 514

Abstract

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the [...] Read more.

The study of upper ocean mixing processes, including their dynamics and thermodynamics, has been a primary focus for oceanographers and meteorologists. Wave breaking in deep water is believed to play a significant role in these processes, affecting air–sea interactions and contributing to the energy dissipation of surface waves. This, in turn, enhances the transfer of gas, heat, and mass at the ocean surface. In this paper, we use the FVCOM-SWAVE coupled wave and current model, which is based on the MY-2.5 turbulent closure model, to examine the response of upper ocean turbulent kinetic energy (TKE) and temperature to various wave breaking parametric schemes. We propose a new parametric scheme for wave breaking energy at the sea surface, which is based on the correlation between breaking wave parameter

R_{B}

and whitecap coverage. The impact of this new wave breaking parametric scheme on the upper ocean under typhoon conditions is analyzed by comparing it with the original parametric scheme that is primarily influenced by wave age. The wave field simulated by SWAVE was verified using Jason-3 satellite altimeter data, confirming the effectiveness of the simulation. The simulation results for upper ocean temperature were also validated using OISST data and Argo float observational data. Our findings indicate that, under the influence of Typhoon Nanmadol, both parametric schemes can transfer the energy of sea surface wave breaking into the seawater. The new wave breaking parameter

R_{B}

scheme effectively enhances turbulent mixing at the ocean surface, leading to a decrease in sea surface temperature (SST) and an increase in mixed layer depth (MLD). This further improves upon the issue of uneven mixing of seawater at the air–sea interface in the MY-2.5 turbulent closure model. However, it is important to note that wave breaking under typhoon conditions is only one aspect of wave impact on ocean disturbances. Therefore, further research is needed to fully understand the impact of waves on upper ocean mixing, including the consideration of other wave mechanisms. Full article

(This article belongs to the Special Issue Advances in Oceanic Dynamics by SAR and Numeric Model in Tropical Cyclone)

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15 pages, 2971 KiB

Open AccessTechnical Note

Coupling Light Intensity and Hyperspectral Reflectance Improve Estimations of the Actual Electron Transport Rate of Mango Leaves (Mangifera indica L.)

by Jia Jin, Quan Wang and Jie Zhuang

Remote Sens. 2024, 16(18), 3523; https://doi.org/10.3390/rs16183523 - 23 Sep 2024

Viewed by 596

Abstract

Real-time and accurate assessment of the photosynthetic rate is of great importance for monitoring the contribution of leaves to the global carbon cycle. The electron transport rate is a critical parameter for accurate simulation of the net photosynthetic rate, which is highly sensitive [...] Read more.

Real-time and accurate assessment of the photosynthetic rate is of great importance for monitoring the contribution of leaves to the global carbon cycle. The electron transport rate is a critical parameter for accurate simulation of the net photosynthetic rate, which is highly sensitive to both light conditions and the biochemical state of the leaf. Although various approaches, including hyperspectral remote sensing techniques, have been proposed so far, the actual electron transport rate is rarely quantified in real time other than being derived from the maximum electron transport (J_max) at a reference temperature in most gas exchange models, leading to the decoupling of gas exchange characteristics from environmental drivers. This study explores the potential of using incident light intensity, hyperspectral reflectance data, and their combination for real-time quantification of the actual electron transport rate (J_a) in mango leaves. The results show that the variations in J_a could be accurately estimated using a combination of incident light intensity and leaf reflectance at 715 nm, with a ratio of performance to deviation (RPD) value of 2.12 (very good predictive performance). Furthermore, the J_a of sunlit leaves can be predicted with an RPD value of about 2.60 using light intensity and a single-band reflectance value within 760–1320 nm, while the actual electron transport rate of shaded leaves can only be predicted with a lower RPD value of 1.73 (fair performance) using light intensity and reflectance at 685 nm. These results offer valuable insights into developing non-destructive, rapid methods for real-time estimation of actual electron transport rates using hyperspectral remote sensing data and incident light conditions. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

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25 pages, 10343 KiB

Open AccessArticle

Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors

by Hang Cao, Hongze Leng, Jun Zhao, Xiaodong Xu, Jinhui Yang, Baoxu Li, Yong Zhou and Lilan Huang

Remote Sens. 2024, 16(18), 3522; https://doi.org/10.3390/rs16183522 - 23 Sep 2024

Viewed by 1004

Abstract

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. [...] Read more.

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data. Full article

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12 pages, 4527 KiB

Open AccessArticle

Observation of Post-Sunset Equatorial Plasma Bubbles with BDS Geostationary Satellites over South China

by Guanyi Ma, Jinghua Li, Jiangtao Fan, Qingtao Wan, Takashi Maruyama, Liang Dong, Yang Gao, Le Zhang and Dong Wang

Remote Sens. 2024, 16(18), 3521; https://doi.org/10.3390/rs16183521 - 23 Sep 2024

Viewed by 520

Abstract

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and [...] Read more.

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night. Full article

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19 pages, 7702 KiB

Open AccessArticle

A Novel Method for Simplifying the Distribution Envelope of Green Tide for Fast Drift Prediction in the Yellow Sea, China

by Yi Ding, Song Gao, Guoman Huang, Lingjuan Wu, Zhiyong Wang, Chao Yuan and Zhigang Yu

Remote Sens. 2024, 16(18), 3520; https://doi.org/10.3390/rs16183520 - 23 Sep 2024

Viewed by 475

Abstract

Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides [...] Read more.

Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas–Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters. Full article

(This article belongs to the Special Issue Remote Sensing for Geology and Mapping (Second Edition))

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21 pages, 7799 KiB

Open AccessArticle

Identification and Characterization of Reclaimed and Underclaimed Mine Features Using Lidar and Temporal Remote Sensing Methods within the Coastal Plain Uranium Mining Region of Texas

by Victoria G. Stengel, Tanya J. Gallegos, Bernard E. Hubbard, Steven M. Cahan and David S. Wallace

Remote Sens. 2024, 16(18), 3519; https://doi.org/10.3390/rs16183519 - 22 Sep 2024

Viewed by 781

Abstract

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic [...] Read more.

We developed a spatiotemporal mapping approach utilizing multiple techniques for distinguishing and mapping known reclaimed mine sites from “unreclaimed” mine sites in a historic uranium mining district along the South Texas Coastal Plains. Lidar laser scanning penetrates the vegetation canopy to expose anthropogenic modifications to the landscape. The Lidar analysis (bare earth elevation surface, slope, topographic contours, topographic textures, and overland-flow hydrography) revealed mine features. Visual interpretation of Landsat imagery and time-series analysis augmented the Lidar analysis revealing the temporal life cycle of mining. The combination of bare earth texture with time-lapse and time-series analyses revealed areas of disturbance for reclaimed mines. The spatiotemporal mapping approach proved to be most useful in identifying and characterizing the known mine pit and pile features, reclamation status, and areas of disturbance due to mining. Two mine waste volume estimation methods resulted in a 21% difference indicating that although the approach helps to map mine features and areas of mining disturbance for the purposes of mine land inventory, additional information is needed to improve the estimation of buried mine waste at reclaimed mine sites. Full article

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19 pages, 2958 KiB

Open AccessArticle

On the Consistency of Stochastic Noise Properties and Velocity Estimations from Different Analysis Strategies and Centers with Environmental Loading and CME Corrections

by Hongli Lv, Xiaoxing He, Shunqiang Hu, Xiwen Sun, Jiahui Huang, Rui Fernandes, Wen Xie and Huajiang Xiong

Remote Sens. 2024, 16(18), 3518; https://doi.org/10.3390/rs16183518 - 22 Sep 2024

Viewed by 624

Abstract

The analysis of the Global Navigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics researcFh. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study [...] Read more.

The analysis of the Global Navigation Satellite System (GNSS) time series provides valuable information for geodesy and geodynamics researcFh. Precise data analysis strategies are crucial for accurately obtaining the linear velocity of GNSS stations, enabling high-precision applications of GNSS time series. This study investigates the impact of different stochastic noise models on velocity estimations derived from GNSS time series, specifically under conditions of environmental loading correction and common mode error (CME) removal. By comparing data from various data centers, we find that post-correction, different analysis strategies exhibit high consistency in their noise characteristics and velocity estimation results. Across various analysis strategies, the optimal noise models were predominantly Power Law with White Noise (PLWN) and Fractional Noise with White Noise (FNWN), with the optimal noise models including COMB/JPL, COMB/SOPAC, and COMB/NGL for approximately 50% of the datasets. Most of the stations (approximately 80%) showed velocity differences below 0.3 mm/year and velocity estimation uncertainties below 0.1 mm/year. Nonetheless, variations in amplitudes and periodic signals persisted due to differences in the processing of raw GNSS observations. For instance, the NGL and JPL datasets, which were processed using GipsyX 2.1 software, showed higher amplitudes of the 5.5-day periodic signal. These findings provide a solid empirical foundation for advancing data analysis methods and enhancing the reliability of GNSS time series results in future research. Full article

(This article belongs to the Special Issue Advances in GNSS for Time Series Analysis)

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26 pages, 36184 KiB

Open AccessArticle

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

by Hao Wu, Chenzuo Ye, Xiangjun Pei, Takashi Oguchi, Zhihao He, Hailong Yang and Runqiu Huang

Remote Sens. 2024, 16(18), 3517; https://doi.org/10.3390/rs16183517 - 22 Sep 2024

Viewed by 900

Abstract

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the [...] Read more.

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 M_W 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management. Full article

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25 pages, 60939 KiB

Open AccessArticle

DETR-ORD: An Improved DETR Detector for Oriented Remote Sensing Object Detection with Feature Reconstruction and Dynamic Query

by Xiaohai He, Kaiwen Liang, Weimin Zhang, Fangxing Li, Zhou Jiang, Zhengqing Zuo and Xinyan Tan

Remote Sens. 2024, 16(18), 3516; https://doi.org/10.3390/rs16183516 - 22 Sep 2024

Viewed by 809

Abstract

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large [...] Read more.

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large sequences of images. Additionally, bipartite graph matching and the limit on the number of queries result in transformer-based detectors performing poorly in scenarios with multiple objects and small object sizes. We propose an improved DETR detector for Oriented remote sensing object detection with Feature Reconstruction and Dynamic Query, termed DETR-ORD. It introduces rotation into the transformer architecture for oriented object detection, reduces computational cost with a hybrid encoder, and includes an IFR (image feature reconstruction) module to address the loss of positional information due to the flattening operation. It also uses ATSS to select auxiliary dynamic training queries for the decoder. This improved DETR-based detector enhances detection performance in challenging oriented optical remote sensing scenarios with similar backbone network parameters. Our approach achieves superior results on most optical remote sensing datasets, such as DOTA-v1.5 (72.07% mAP) and DIOR-R (66.60% mAP), surpassing the baseline detector. Full article

(This article belongs to the Section AI Remote Sensing)

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26 pages, 29457 KiB

Open AccessArticle

SSBAS-InSAR: A Spatially Constrained Small Baseline Subset InSAR Technique for Refined Time-Series Deformation Monitoring

by Zhigang Yu, Guanghui Zhang, Guoman Huang, Chunquan Cheng, Zhuopu Zhang and Chenxi Zhang

Remote Sens. 2024, 16(18), 3515; https://doi.org/10.3390/rs16183515 - 22 Sep 2024

Viewed by 1308

Abstract

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation [...] Read more.

SBAS-InSAR technology is effective in obtaining surface deformation information and is widely used in monitoring landslides and mining subsidence. However, SBAS-InSAR technology is susceptible to various errors, including atmospheric, orbital, and phase unwrapping errors. These multiple errors pose significant challenges to precise deformation monitoring over large areas. This paper examines the spatial characteristics of these errors and introduces a spatially constrained SBAS-InSAR method, termed SSBAS-InSAR, which enhances the accuracy of wide-area surface deformation monitoring. The method employs multiple stable ground points to create a control network that limits the propagation of multiple types of errors in the interferometric unwrapped data, thereby reducing the impact of long-wavelength signals on local deformation measurements. The proposed method was applied to Sentinel-1 data from parts of Jining, China. The results indicate that, compared to the traditional SBAS-InSAR method, the SSBAS-InSAR method significantly reduced phase closure errors, deformation rate standard deviations, and phase residues, improved temporal coherence, and provided a clearer representation of deformation in time-series curves. This is crucial for studying surface deformation trends and patterns and for preventing related disasters. Full article

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21 pages, 4905 KiB

Open AccessArticle

Analysis of Spatiotemporal Changes in Energy Consumption Carbon Emissions at District and County Levels Based on Nighttime Light Data—A Case Study of Jiangsu Province in China

by Chengzhi Xiang, Yong Mei and Ailin Liang

Remote Sens. 2024, 16(18), 3514; https://doi.org/10.3390/rs16183514 - 22 Sep 2024

Cited by 1 | Viewed by 802

Abstract

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large [...] Read more.

Approximately 86% of the total carbon emissions are generated by energy consumption, and the study of the variation of energy consumption carbon emissions (ECCE) is of vital significance to regional sustainable development and energy conservation. Currently, carbon emissions accounting mainly focuses on large and medium-scale statistics, but at smaller scales (district and county level), it still remains unclear. Due to the high correlation between nighttime light (NTL) data and ECCE, this study combines “energy inventory statistics” with NTL data to estimate ECCE at smaller scales. First, we obtained city-level statistics on ECCE and corrected the NTL data by applying the VANUI index to the original NTL data from NPP-VIIRS. Second, an analysis was conducted on the correlation between the two variables, and a model was created to fit the relationship between them. Under the assumption that ECCE will be consistent within a given region, we utilized the model to estimate ECCE in districts and counties, eventually obtaining correct results at the county-level. We estimated the ECCE in each district and county of Jiangsu Province from 2013 to 2022 using the above-proposed approach, and we examined the variations in these emissions both spatially and temporally across the districts and counties. The results revealed a significant degree of correlation between the two variables, with the R² of the fitting models exceeding 0.8. Furthermore, ECCE in Jiangsu Province fluctuated upward during this period, with clear regional clustering characteristics. The study’s conclusions provide information about how carbon emissions from small-scale energy use are estimated. They also serve as a foundation for the creation of regional energy conservation and emission reduction policies, as well as a small-scale assessment of the present state. Full article

(This article belongs to the Section Environmental Remote Sensing)

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Remote Sens., Volume 16, Issue 18 (September-2 2024) – 190 articles

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