Remote Sensing

24 pages, 1756 KiB

Open AccessArticle

Dual-Branch Seasonal Error Elimination Change Detection Framework Using Target Image Feature Fusion Generator

by Hongming Zhu, Jipeng Zhang, Zeju Wang, Xinyu Liu, Qin Liu and Bowen Du

Remote Sens. 2025, 17(3), 523; https://doi.org/10.3390/rs17030523 (registering DOI) - 3 Feb 2025

In change detection tasks, seasonal variations in spectral characteristics and surface cover can negatively impact performance when comparing image pairs from different seasons. Many existing change detection methods do not specifically address the performance degradation caused by seasonal errors. To tackle this issue, [...] Read more.

In change detection tasks, seasonal variations in spectral characteristics and surface cover can negatively impact performance when comparing image pairs from different seasons. Many existing change detection methods do not specifically address the performance degradation caused by seasonal errors. To tackle this issue, the Dual-Branch Seasonal Error Elimination Change Detection Framework using Target Image Feature Fusion Generator (DBSEE-CDF) is introduced. Specifically, the approach utilizes the Target Image Feature Fusion Generator (TIFFG), which incorporates spatial and channel attention mechanisms to extract features from target images and integrates them with deep features from input images using cross-attention. To avoid a severe loss of visual fidelity caused by the significant differences in texture and color features between snow-covered and snow-free images, as well as the different requirements for style transformation, different generators for snow-covered winter images and snow-free winter images are employed to produce intermediate images that eliminate seasonal errors before conducting change detection tasks. The experimental results demonstrate significant improvements in both quantitative and qualitative assessments of change detection tasks compared to directly performing change detection with various models, highlighting the effectiveness of the proposed DBSEE-CDF. Full article

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22 pages, 29747 KiB

Open AccessArticle

An Integrated Method for Inverting Beach Surface Moisture by Fusing Unmanned Aerial Vehicle Orthophoto Brightness with Terrestrial Laser Scanner Intensity

by Jun Zhu, Kai Tan, Feijian Yin, Peng Song and Faming Huang

Remote Sens. 2025, 17(3), 522; https://doi.org/10.3390/rs17030522 (registering DOI) - 3 Feb 2025

Abstract

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a [...] Read more.

Beach surface moisture (BSM) is crucial to studying coastal aeolian sand transport processes. However, traditional measurement techniques fail to accurately monitor moisture distribution with high spatiotemporal resolution. Remote sensing technologies have garnered widespread attention for providing rapid and non-contact moisture measurements, but a single method has inherent limitations. Passive remote sensing is challenged by complex beach illumination and sediment grain size variability. Active remote sensing represented by LiDAR (light detection and ranging) exhibits high sensitivity to moisture, but requires cumbersome intensity correction and may leave data holes in high-moisture areas. Using machine learning, this research proposes a BSM inversion method that fuses UAV (unmanned aerial vehicle) orthophoto brightness with intensity recorded by TLSs (terrestrial laser scanners). First, a back propagation (BP) network rapidly corrects original intensity with in situ scanning data. Second, beach sand grain size is estimated based on the characteristics of the grain size distribution. Then, by applying nearest point matching, intensity and brightness data are fused at the point cloud level. Finally, a new BP network coupled with the fusion data and grain size information enables automatic brightness correction and BSM inversion. A field experiment at Baicheng Beach in Xiamen, China, confirms that this multi-source data fusion strategy effectively integrates key features from diverse sources, enhancing the BP network predictive performance. This method demonstrates robust predictive accuracy in complex beach environments, with an RMSE of 2.63% across 40 samples, efficiently producing high-resolution BSM maps that offer values in studying aeolian sand transport mechanisms. Full article

(This article belongs to the Section Ocean Remote Sensing)

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27 pages, 3819 KiB

Open AccessArticle

Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning

by Lei Liu, Minghua Wu, Dongyang Cheng and Wei Wang

Remote Sens. 2025, 17(3), 521; https://doi.org/10.3390/rs17030521 (registering DOI) - 3 Feb 2025

Abstract

With advancements in phased array and cognitive technologies, the adaptability of modern multifunction radars (MFRs) has significantly improved, enabling greater flexibility in waveform parameters and beam scheduling. However, these enhancements have made it increasingly difficult to establish fixed relationships between working modes using [...] Read more.

With advancements in phased array and cognitive technologies, the adaptability of modern multifunction radars (MFRs) has significantly improved, enabling greater flexibility in waveform parameters and beam scheduling. However, these enhancements have made it increasingly difficult to establish fixed relationships between working modes using traditional radar recognition methods. Furthermore, conventional approaches often exhibit limited robustness and computational efficiency in complex or noisy environments. To address these challenges, this paper proposes a joint learning framework based on a hybrid model combining convolutional neural networks (CNNs) and Transformers for MFR working mode recognition. This hybrid model leverages the local convolution operations of the CNN module to extract local characters from radar pulse sequences, capturing the dynamic patterns of radar waveforms across different modes. Simultaneously, the multi-head attention mechanism in the Transformer module models long-range dependencies within the sequences, capturing the “semantic information” of waveform scheduling intrinsic to MFR behavior. By integrating characters across multiple levels, the hybrid model effectively recognizes MFR working modes. This study used the data of the Mercury MFR for modeling and simulation, and proved through a large number of experiments that the proposed hybrid model can achieve robust and reliable identification of advanced MFR working modes even in complex electromagnetic environments. Full article

25 pages, 9099 KiB

Open AccessArticle

A Universal Framework for Near-Real-Time Detection of Vegetation Anomalies from Landsat Data

by Yixuan Xie, Zhiqiang Xiao, Juan Li, Jinling Song, Hua Yang and Kexin Lv

Remote Sens. 2025, 17(3), 520; https://doi.org/10.3390/rs17030520 (registering DOI) - 3 Feb 2025

Abstract

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods [...] Read more.

Vegetation anomalies are frequently occurring and may greatly affect ecological functions. Many near-real-time (NRT) detection methods have been developed to detect these anomalies in a timely manner whenever a new satellite observation is available. However, the undisturbed vegetation conditions captured by these methods are only applicable to a particular pixel or vegetation type, resulting in a lack of universality. Also, most methods that use single characteristic parameter may ignore the multi-spectral expression of vegetation anomalies. In this study, we developed a universal framework to simultaneously detect various vegetation anomalies in NRT from Landsat observations. Firstly, Landsat surface reflectance data from the Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites were selected as a reference vegetation dataset to calculate the normalized difference vegetation index (NDVI) and the normalized burn ratio (NBR), which describe vegetation conditions from the perspectives of greenness and moisture, respectively. After the elimination of cloud-contaminated pixels, the high-quality NDVI and NBR data over the BELMANIP sites were further normalized in order to remove the differences in the growth of the varying vegetation. Based on the normalized NDVI and NBR, kernel density estimation (KDE) was used to create a universal measure of undisturbed vegetation, which described the uniform spectral frequency distribution of different undisturbed vegetation with a series of accumulated probabilities on a monthly basis. Whenever a new Landsat observation is collected, the vegetation anomalies are determined according to the universal measure in NRT. To demonstrate the potential of this framework, three study areas with different anomaly types (deforestation, fire event, and insect outbreak) in distinct ecozones (rainforest, coniferous forest, and deciduous broad-leaf forest) were used. The quantitative analyses showed generally high overall accuracies (>90% with the kappa >0.82). The user accuracy for the fire event and the producer accuracy for the earlier insect infestation were relatively lower. The accuracies may be affected by the complexity of the land surface, the quality of the Landsat image, and the accumulated probability threshold. Full article

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26 pages, 24954 KiB

Open AccessArticle

Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet)

by Minjie Deng, Yong Han, Yan Liu, Li Dong, Qicheng Zhou, Yurong Zhang, Ximing Deng and Tianwei Lu

Remote Sens. 2025, 17(3), 519; https://doi.org/10.3390/rs17030519 (registering DOI) - 3 Feb 2025

Abstract

Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex [...] Read more.

Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex terrains. Machine learning approaches based on two-dimensional images face challenges such as data scarcity and high annotation costs, which hinder accurate pixel-level cloud identification. Additionally, single-pixel classification methods fail to effectively exploit the spatial correlations inherent in cloud structures. In this paper, we introduce the one-dimensional nested U-Net cloud-classification model (1D-CloudNet), which was developed using Himawari-8 and CloudSat data collected over two years (2016–2017), comprising a total of 27,688 samples. This model is explicitly tailored for the analysis of one-dimensional, multi-channel images. Experimental results indicate that 1D-CloudNet achieves an overall classification accuracy of 88.19% during the day and 87.40% at night. This represents a 3–4% improvement compared to traditional models. The model demonstrates robust performance for both daytime and nighttime applications, effectively addressing the absence of nighttime data in the Himawari-8 L2 product. In the future, 1D-CloudNet is expected to support regional climate research and extreme weather monitoring. Further optimization could enhance its adaptability to complex terrains. Full article

(This article belongs to the Special Issue Applications and Analysis of Satellite Cloud Imagery Using Deep Learning Techniques)

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11 pages, 2174 KiB

Open AccessTechnical Note

Using Night-Time Drone-Acquired Thermal Imagery to Monitor Flying-Fox Productivity—A Proof of Concept

by Jessica Meade, Eliane D. McCarthy, Samantha H. Yabsley, Sienna C. Grady, John M. Martin and Justin A. Welbergen

Remote Sens. 2025, 17(3), 518; https://doi.org/10.3390/rs17030518 (registering DOI) - 3 Feb 2025

Abstract

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. Species, such as bats, that have slow life histories, characterized by extended lifespans and low reproductive rates, are particularly vulnerable to environmental changes, stochastic events, [...] Read more.

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. Species, such as bats, that have slow life histories, characterized by extended lifespans and low reproductive rates, are particularly vulnerable to environmental changes, stochastic events, and human activities. An accurate assessment of productivity can improve parameters for population modelling and provide insights into species’ capacity to recover from population perturbations, yet data on reproductive output are often lacking. Recently, advances in drone technology have allowed for the development of a drone-based thermal remote sensing technique to accurately and precisely count the numbers of flying-foxes (Pteropus spp.) in their tree roosts. Here, we extend that method and use a drone-borne thermal camera flown at night to count the number of flying-fox pups that are left alone in the roost whilst their mothers are out foraging. We show that this is an effective method of estimating flying-fox productivity on a per-colony basis, in a standardized fashion, and at a relatively low cost. When combined with a day-time drone flight used to estimate the number of adults in a colony, this can also provide an estimate of female reproductive performance, which is important for assessments of population health. These estimates can be related to changes in local food availability and weather conditions (including extreme heat events) and enable us to determine, for the first time, the impacts of disturbances from site-specific management actions on flying-fox population trajectories. Full article

(This article belongs to the Special Issue Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals)

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25 pages, 14032 KiB

Open AccessArticle

Sea Surface Temperature Forecasting Using Foundational Models: A Novel Approach Assessed in the Caribbean Sea

by David Francisco Bustos Usta, Lien Rodríguez-López, Rafael Ricardo Torres Parra and Luc Bourrel

Remote Sens. 2025, 17(3), 517; https://doi.org/10.3390/rs17030517 (registering DOI) - 2 Feb 2025

Abstract

Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of [...] Read more.

Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002–2023) of high-resolution satellite-derived and in situ data. The Chronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed Lag-Llama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate SST variability. The Chronos model’s ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern Caribbean upwelling system. The Chronos forecast resembles SST variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. Beyond this, the model exhibits increased bias and error, particularly in regions with strong SST gradients and high variability associated with coastal upwelling processes. The findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. This study establishes a benchmark for SST forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions. Full article

(This article belongs to the Special Issue Advanced Applications of Remote Sensing in Monitoring Marine Environment (Second Edition))

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14 pages, 31482 KiB

Open AccessTechnical Note

A Three-DimensionalImaging Method for Unmanned Aerial Vehicle-Borne SAR Based on Nested Difference Co-Arrays and Azimuth Multi-Snapshots

by Ruizhe Shi, Yitong Luo, Zhe Zhang, Xiaolan Qiu and Chibiao Ding

Remote Sens. 2025, 17(3), 516; https://doi.org/10.3390/rs17030516 (registering DOI) - 2 Feb 2025

Abstract

Due to its miniature size and single-pass nature, Unmanned Aerial Vehicle (UAV)-borne array synthetic aperture radar (SAR) is capable of obtaining three-dimensional (3D) electromagnetic scattering information with a low cost and high efficiency, making it widely applicable in various fields. However, the limited [...] Read more.

Due to its miniature size and single-pass nature, Unmanned Aerial Vehicle (UAV)-borne array synthetic aperture radar (SAR) is capable of obtaining three-dimensional (3D) electromagnetic scattering information with a low cost and high efficiency, making it widely applicable in various fields. However, the limited payload capacity of the UAV platform results in a limited number of array antennas and affects 3D resolution. This paper proposes a 3D imaging method for UAV-borne SAR based on nested difference co-arrays and azimuth multi-snapshots. We first designed an antenna arrangement based on nested arrays, generating a virtual antenna twice as long as the original one. Then, we used a difference co-array method for 3D imaging. The required multi-snapshot data were obtained through azimuth down-sampling, rather than traditional spatial averaging methods. Due to the slow flight of the UAV, this method could generate multiple SAR images without affecting the two-dimensional resolution. Based on simulations and real data verification, the proposed algorithm overcomes the problem of two-dimensional resolution decline caused by traditional spatial averaging methods and improves three-dimensional resolution ability, theoretically achieving half the Rayleigh resolution. Full article

26 pages, 12784 KiB

Open AccessArticle

Advanced Deep Learning Approaches for Forecasting High-Resolution Fire Weather Index (FWI) over CONUS: Integration of GNN-LSTM, GNN-TCNN, and GNN-DeepAR

by Shihab Ahmad Shahriar, Yunsoo Choi and Rashik Islam

Remote Sens. 2025, 17(3), 515; https://doi.org/10.3390/rs17030515 (registering DOI) - 1 Feb 2025

Abstract

Wildfires in the United States have increased in frequency and severity over recent decades, driven by climate change, altered weather patterns, and accumulated flammable materials. Accurately forecasting the Fire Weather Index (FWI) is crucial for mitigating wildfire risks and protecting ecosystems, human health, [...] Read more.

Wildfires in the United States have increased in frequency and severity over recent decades, driven by climate change, altered weather patterns, and accumulated flammable materials. Accurately forecasting the Fire Weather Index (FWI) is crucial for mitigating wildfire risks and protecting ecosystems, human health, and infrastructure. This study analyzed FWI trends across the Continental United States (CONUS) from 2014 to 2023, using meteorological data from the gridMET dataset. Key variables, including temperature, relative humidity, wind speed, and precipitation, were utilized to calculate the FWI at a fine spatial resolution of 4 km, ensuring the precise identification of wildfire-prone areas. Based on this, our study developed a hybrid modeling framework to forecast FWI over a 14-day horizon, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR). The models were evaluated using the Index of Agreement (IOA) and root mean squared error (RMSE). The results revealed that the Southwest and West regions of CONUS consistently exhibited the highest mean FWI values, with the summer months demonstrating the greatest variability across all climatic zones. In terms of model performance on forecasting, Day 1 results highlighted the superior performance of the GNN-TCNN model, achieving an IOA of 0.95 and an RMSE of 1.21, compared to the GNN-LSTM (IOA: 0.93, RMSE: 1.25) and GNN-DeepAR (IOA: 0.92, RMSE: 1.30). On average, across all 14 days, the GNN-TCNN outperformed others with a mean IOA of 0.885 and an RMSE of 1.325, followed by the GNN-LSTM (IOA: 0.852, RMSE: 1.590) and GNN-DeepAR (IOA: 0.8225, RMSE: 1.755). The GNN-TCNN demonstrated robust accuracy across short-term (days 1–7) and long-term (days 8–14) forecasts. This study advances wildfire risk assessment by combining descriptive analysis with hybrid modeling, offering a scalable and robust framework for FWI forecasting and proactive wildfire management amidst a changing climate. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis II)

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20 pages, 10176 KiB

Open AccessArticle

DHQ-DETR: Distributed and High-Quality Object Query for Enhanced Dense Detection in Remote Sensing

by Chenglong Li, Jianwei Zhang, Bihan Huo and Yingjian Xue

Remote Sens. 2025, 17(3), 514; https://doi.org/10.3390/rs17030514 (registering DOI) - 1 Feb 2025

Abstract

With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their [...] Read more.

With the widespread application of remote sensing technologies and UAV imagery in various fields, dense object detection has become a significant and challenging task in computer vision research. Existing end-to-end detection models, particularly those based on DETR, often face criticism due to their high computational demands, slow convergence rates, and inadequacy in managing dense, multi-scale objects. These challenges are especially acute in remote sensing applications, where accurate analysis of large-scale aerial and satellite imagery relies heavily on effective dense object detection. In this paper, we propose the DHQ-DETR framework, which addresses these issues by modeling bounding box offsets as distributions. DHQ-DETR incorporates the Distribution Focus Loss (DFL) to enhance residual learning, and introduces a High-Quality Query Selection (HQQS) module to effectively balance classification and regression tasks. Additionally, we propose an auxiliary detection head and a sample assignment strategy that complements the Hungarian algorithm to accelerate convergence. Our experimental results demonstrate the superior performance of DHQ-DETR, achieving an average precision (AP) of 53.7% on the COCO val2017 dataset, 54.3% on the DOTAv1.0, and 32.4% on Visdrone, underscoring its effectiveness for real-world dense object detection tasks. Full article

(This article belongs to the Special Issue Multi-platform and Multi-modal Remote Sensing Data Fusion with Advanced Deep Learning Techniques (Second Edition))

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24 pages, 3576 KiB

Open AccessArticle

Preliminary Trajectory Analysis of CubeSats with Electric Thrusters in Nodal Flyby Missions for Asteroid Exploration

by Alessandro A. Quarta

Remote Sens. 2025, 17(3), 513; https://doi.org/10.3390/rs17030513 (registering DOI) - 1 Feb 2025

Abstract

This paper studies the performance of an interplanetary CubeSat equipped with a continuous-thrust primary propulsion system in a heliocentric mission scenario, which models a nodal flyby with a potential near-Earth asteroid. In particular, the mathematical model discussed in this work considers a small [...] Read more.

This paper studies the performance of an interplanetary CubeSat equipped with a continuous-thrust primary propulsion system in a heliocentric mission scenario, which models a nodal flyby with a potential near-Earth asteroid. In particular, the mathematical model discussed in this work considers a small array of (commercial) miniaturized electric thrusters installed onboard a typical CubeSat, whose power-generation system is based on the use of classic solar panels. The paper also discusses the impact of the size of thrusters’ array on the nominal performance of the transfer mission by analyzing the trajectory of the CubeSat from an optimization point of view. In this context, the propulsive characteristics of a commercial electric thruster which corresponds to a iodine-fueled gridded ion-propulsion system are considered in this study, while the proposed procedure can be easily extended to a generic continuous-thrust propulsion system whose variation in thrust magnitude and specific impulse as a function of the input electric power is a known analytic function. Using an indirect approach, the paper illustrates the optimal guidance law, which allows the interplanetary CubeSat to reach a given solar distance, with the minimum flight time, by starting from a circular (ecliptic) parking orbit of assigned radius. The mission scenario is purely two-dimensional and models a rapid nodal flyby with a near-Earth asteroid whose nodal distance coincides with the solar distance to be reached. Full article

(This article belongs to the Special Issue Advances in Exploring the Moon, Mars, and Asteroids Based on In-Situ and Remote Sensing Measurements)

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29 pages, 34420 KiB

Open AccessArticle

Evaluating Generalization of Methods for Artificially Generating NDVI from UAV RGB Imagery in Vineyards

by Jurrian Doornbos, Önder Babur and João Valente

Remote Sens. 2025, 17(3), 512; https://doi.org/10.3390/rs17030512 (registering DOI) - 1 Feb 2025

Abstract

High-resolution NDVI maps derived from UAV imagery are valuable in precision agriculture, supporting vineyard management decisions such as disease risk and vigor assessments. However, the expense and complexity of multispectral sensors limit their widespread use. In this study, we evaluate Generative Adversarial Network [...] Read more.

High-resolution NDVI maps derived from UAV imagery are valuable in precision agriculture, supporting vineyard management decisions such as disease risk and vigor assessments. However, the expense and complexity of multispectral sensors limit their widespread use. In this study, we evaluate Generative Adversarial Network (GAN) approaches—trained on either multispectral-derived or true RGB data—to convert low-cost RGB imagery into NDVI maps. We benchmark these models against simpler, explainable RGB-based indices (RGBVI, vNDVI) using Botrytis bunch rot (BBR) risk and vigor mapping as application-centric tests. Our findings reveal that both multispectral- and RGB-trained GANs can generate NDVI maps suitable for BBR risk modelling, achieving R-squared values between 0.8 and 0.99 on unseen datasets. However, the RGBVI and vNDVI indices often match or exceed the GAN outputs, for vigor mapping. Moreover, model performance varies with sensor differences, vineyard structures, and environmental conditions, underscoring the importance of training data diversity and domain alignment. In highlighting these sensitivities, this application-centric evaluation demonstrates that while GANs can offer a viable NDVI alternative in some scenarios, their real-world utility is not guaranteed. In many cases, simpler RGB-based indices may provide equal or better results, suggesting that the choice of NDVI conversion method should be guided by both application requirements and the underlying characteristics of the subject matter. Full article

13 pages, 2080 KiB

Open AccessCommunication

Mesosphere and Lower Thermosphere (MLT) Density Responses to the May 2024 Superstorm at Mid-to-High Latitudes in the Northern Hemisphere Based on Sounding of the Atmosphere Using Broadband Emission Radiometry (SABER) Observations

by Ningtao Huang, Jingyuan Li, Jianyong Lu, Shuai Fu, Meng Sun, Guanchun Wei, Mingming Zhan, Ming Wang and Shiping Xiong

Remote Sens. 2025, 17(3), 511; https://doi.org/10.3390/rs17030511 (registering DOI) - 31 Jan 2025

Abstract

The thermospheric density response during geomagnetic storms has been extensively explored, but with limited studies on the density response in the Mesosphere and Lower Thermosphere (MLT) region. In this study, the density response in the MLT region at mid-to-high latitudes of the Northern [...] Read more.

The thermospheric density response during geomagnetic storms has been extensively explored, but with limited studies on the density response in the Mesosphere and Lower Thermosphere (MLT) region. In this study, the density response in the MLT region at mid-to-high latitudes of the Northern Hemisphere during the intense geomagnetic storm in May 2024 is investigated using density data from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument aboard the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite. The results indicate that during the geomagnetic storm, the density response exhibits both significant decreases and increases; specifically, approximately 25.2% of the observation points show a notable reduction within a single day, with the maximum decrease exceeding −59.9% at 105 km. In contrast, around 16.5% of the observation points experience a significant increase over the same period, with the maximum increase surpassing 82.4% at 105 km. The distribution of density changes varies with altitudes. The magnitude of density increases diminishes with decreasing altitude, whereas the density decreases exhibit altitude-dependent intensity variations. Density decreases are primarily concentrated in high-latitude regions, especially in the polar cap, while density increases are mainly observed between 50°N and 70°N. The intensity of density response is generally stronger in the dusk sector than in the dawn sector. These results suggest that atmospheric expansion and uplift driven by temperature variations are the primary factors underlying the observed density change. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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20 pages, 2021 KiB

Open AccessArticle

Improving BeiDou Global Navigation Satellite System (BDS-3)-Derived Station Coordinates Using Calibrated Satellite Antennas and Station Inter-System Translation Parameters

by Tao Zhang, Shiwei Guo, Lei Fan and Chuang Shi

Remote Sens. 2025, 17(3), 510; https://doi.org/10.3390/rs17030510 (registering DOI) - 31 Jan 2025

Abstract

The BeiDou global navigation satellite system (BDS-3) has been widely applied in various geodetic applications since its full operation. However, the estimated station coordinates using BDS-3 are less precise compared to GPS results. It contains systematic errors caused by scale bias with respect [...] Read more.

The BeiDou global navigation satellite system (BDS-3) has been widely applied in various geodetic applications since its full operation. However, the estimated station coordinates using BDS-3 are less precise compared to GPS results. It contains systematic errors caused by scale bias with respect to International GNSS Service (IGS) 2020 frame and Inter-System Translation Parameters (ISTPs). In order to improve the consistency of BDS-3-derived station coordinates with respect to IGS20 products, we firstly estimated the satellite antenna Phase Center Offsets (PCOs) for BDS-3 Medium Earth Orbit (MEO) constellation, and then estimated station-specific ISTPs from GPS to BDS-3 systems. The results indicate that the PCO-Z estimates show large differences among satellites from different manufacturers and orbit planes. The estimated BDS-3 satellite PCOs exhibit a systematic bias of −9.3 cm in the Z-direction compared to ground calibrations. The maximum mean station-specific ISTPs can reach up to 3 mm, highlighting significant variability and the need for refinement in positioning. When using the estimated PCOs instead of igs20.atx values, the estimated scale bias with respect to the IGS20 frame is reduced from 0.38 ppb to −0.12 ppb, indicating that the refined BDS-3 satellite PCOs are well compatible with IGS20. Regarding the Up component that is correlated with the scale factor, the station coordinate differences with respect to the IGS20 frame is reduced from 7.0 mm to 6.2 mm in terms of the root mean square (RMS), which is improved by 11.4%. Considering the additional ISTP corrections, a further improvement of 17% was obtained in station coordinates. The RMS of station coordinate differences with respect to the IGS20 frame is 2.3 mm, 2.7 mm, and 5.2 mm for the North, East, and Up components, respectively. Full article

(This article belongs to the Special Issue Beidou/GNSS Positioning, Navigation and Timing: Methods and Technology (Second Edition))

20 pages, 19844 KiB

Open AccessArticle

Automatic Detection of War-Destroyed Buildings from High-Resolution Remote Sensing Images

by Yu Wang, Yue Li and Shufeng Zhang

Remote Sens. 2025, 17(3), 509; https://doi.org/10.3390/rs17030509 (registering DOI) - 31 Jan 2025

Abstract

Modern high-intensity armed conflicts often lead to extensive damage to urban infrastructure. The use of high-resolution remote sensing images can clearly detect damage to individual buildings which is of great significance for monitoring war crimes and damage assessments that destroy civilian infrastructure indiscriminately. [...] Read more.

Modern high-intensity armed conflicts often lead to extensive damage to urban infrastructure. The use of high-resolution remote sensing images can clearly detect damage to individual buildings which is of great significance for monitoring war crimes and damage assessments that destroy civilian infrastructure indiscriminately. In this paper, we propose SOCA-YOLO (Sampling Optimization and Coordinate Attention–YOLO), an automatic detection method for destroyed buildings in high-resolution remote sensing images based on deep learning techniques. First, based on YOLOv8, Haar wavelet transform and convolutional blocks are used to downsample shallow feature maps to make full use of spatial details in high-resolution remote sensing images. Second, the coordinate attention mechanism is integrated with C2f so that the network can use the spatial information to enhance the feature representation earlier. Finally, in the feature fusion stage, a lightweight dynamic upsampling strategy is used to improve the difference in the spatial boundaries of feature maps. In addition, this paper obtained high-resolution remote sensing images of urban battlefields through Google Earth, constructed a dataset for the detection of objects on buildings, and conducted training and verification. The experimental results show that the proposed method can effectively improve the detection accuracy of destroyed buildings, and the method is used to map destroyed buildings in cities such as Mariupol and Volnovaja where violent armed conflicts have occurred. The results show that deep learning-based object detection technology has the advantage of fast and accurate detection of destroyed buildings caused by armed conflict, which can provide preliminary reference information for monitoring war crimes and assessing war losses. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

22 pages, 1458 KiB

Open AccessArticle

Estimating Olive Tree Density in Delimited Areas Using Sentinel-2 Images

by Adolfo Lozano-Tello, Jorge Luceño, Andrés Caballero-Mancera and Pedro J. Clemente

Remote Sens. 2025, 17(3), 508; https://doi.org/10.3390/rs17030508 - 31 Jan 2025

Abstract

The objective of this study is to develop a method for estimating the density of olive trees in delimited plots using low-resolution images from the Sentinel-2 satellite. This approach is particularly relevant in certain regions where high-resolution orthophotos, which are often costly and [...] Read more.

The objective of this study is to develop a method for estimating the density of olive trees in delimited plots using low-resolution images from the Sentinel-2 satellite. This approach is particularly relevant in certain regions where high-resolution orthophotos, which are often costly and not always available, cannot be accessed. This study focuses on the Extremadura region in Spain, where 48,530 olive plots were analysed. Data from Sentinel-2’s multispectral bands were obtained for each plot, and a Random Forest Regression (RFR) model was used to correlate these values with the number of olive trees, previously counted from orthophotos using machine learning object detection techniques. The results show that the proposed method can predict olive tree density within an acceptable error margin, which is especially useful for distinguishing plots with a density greater than 300 olive trees per hectare—a key criterion for allocating agricultural subsidies in the region. Although the accuracy of the model is not optimal, an average error of ±15.04 olive trees per hectare makes it a viable tool for practical applications where extreme precision is not required. The developed method may also be extrapolated to other cases and crop types, such as fruit trees or forest masses, offering an efficient solution for annual density estimates without relying on costly aerial images. Future research could enhance the accuracy of the model by grouping plots according to additional characteristics, such as tree size or plantation type. Full article

(This article belongs to the Special Issue Recent Advances in Remote Sensing Image Processing Technology)

28 pages, 15985 KiB

Open AccessArticle

Approximate Near-Real-Time Assessment of Some Characteristic Parameters of the Spring Ozone Depletion over Antarctica Using Ground-Based Measurements

by Boyan H. Petkov, Vito Vitale, Piero Di Carlo, Héctor A. Ochoa, Adriana Gulisano, Iona L. Coronato, Kamil Láska, Ivan Kostadinov, Angelo Lupi, Mauro Mazzola, Alice Cavaliere, Claudia Frangipani, Giulio Verazzo and Simone Pulimeno

Remote Sens. 2025, 17(3), 507; https://doi.org/10.3390/rs17030507 - 31 Jan 2025

Abstract

The strong Antarctic vortex plays a crucial role in forming an expansive region with significant stratospheric ozone depletion during austral spring, commonly referred to as the Antarctic “ozone hole”. This study examines daily ozone column behavior during this phenomenon using ERA5 reanalysis data [...] Read more.

The strong Antarctic vortex plays a crucial role in forming an expansive region with significant stratospheric ozone depletion during austral spring, commonly referred to as the Antarctic “ozone hole”. This study examines daily ozone column behavior during this phenomenon using ERA5 reanalysis data and ground-based observations from 10 Antarctic stations collected between September and December from 2008 to 2022. A preliminary analysis of these datasets revealed smoothly varying patterns with quasi-uniform gradients in the ozone distribution within the ozone hole. This observation led to the hypothesis that average ozone columns over zones, defined as concentric areas around the South Pole, can be estimated using mean values of the measurements derived from station observations. This study aims to evaluate the validity of this hypothesis. The results indicate that the mean ozone levels calculated from daily measurements at two stations—Belgrano and Dome Concordia, or Belgrano and Arrival Heights—provide a reliable approximation of the average ozone levels over the zone spanning 70°S to 90°S. Including additional stations extended the zone of reliable approximation northward to 58°S. The approximation error was estimated to range from 5% to 7% at 1σ and from 6% to 8% at the 10th–90th percentile levels. Furthermore, the geographical distribution of the stations enabled a schematic reconstruction of the ozone hole’s position and shape. On the other hand, the high frequency of ground-based measurements contributed to studying the ozone hole variability in both the inner area and edges on an hourly time scale. These findings have practical implications for the near-real-time monitoring of ozone hole development, along with satellite observations, considering ground-based measurements as a source of information about ozone layer in the South Pole region. The results also suggest the possible role of observations from the ground in the analyses of pre-satellite-era hole behavior. Additionally, this study found a high degree of consistency between ground-based measurements and corresponding ERA5 reanalysis data, further supporting the reliability of the observations. Full article

25 pages, 7531 KiB

Open AccessArticle

Lidar Doppler Tomography Focusing Error Analysis and Focusing Method for Targets with Unknown Rotational Speed

by Yutang Li, Chen Xu, Dengfeng Liu, Anpeng Song, Jian Li, Dongzhe Han, Kai Jin, Youming Guo and Kai Wei

Remote Sens. 2025, 17(3), 506; https://doi.org/10.3390/rs17030506 - 31 Jan 2025

Abstract

Lidar Doppler tomography (LDT) is a significant method for imaging rotating targets in long-distance air and space applications. Typically, these targets are non-cooperative and exhibit unknown rotational speeds. Inferring the rotational speed from observational data is essential for effective imaging. However, existing research [...] Read more.

Lidar Doppler tomography (LDT) is a significant method for imaging rotating targets in long-distance air and space applications. Typically, these targets are non-cooperative and exhibit unknown rotational speeds. Inferring the rotational speed from observational data is essential for effective imaging. However, existing research predominantly emphasizes the development of imaging algorithms and interference suppression, often neglecting the analysis of rotational speed estimation. This paper examines the impact of errors in rotational speed estimation on imaging quality and proposes a robust method for accurate speed estimation that yields focused imaging results. We developed a specialized measurement matrix to characterize the imaging process, which effectively captures the variations in measurement matrices resulting from different rotational speed estimates. We refer to this variation as the law of spatiotemporal propagation of errors, indicating that both the imaging accumulation time and the spatial distribution of the target influence the error distribution of the measurement matrix. Furthermore, we validated this principle through imaging simulations of point and spatial targets. Additionally, we present a method for estimating rotational speed, which includes a coarse estimation phase, image filtering, and a fine estimation phase utilizing Rényi entropy minimization. The initial rough estimate is derived from the periodicity observed in the echo time-frequency distribution. The image filtering process leverages the spatial regularity of the measurement matrix’s error distribution. The precise estimation of rotational speed employs Rényi entropy to assess image quality, thereby enhancing estimation accuracy. We constructed a Lidar Doppler tomography system and validated the effectiveness of the proposed method through close-range experiments. The system achieved a rotational speed estimation accuracy of 97.81%, enabling well-focused imaging with a spatial resolution better than 1 mm. Full article

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29 pages, 18931 KiB

Open AccessArticle

OSNet: An Edge Enhancement Network for a Joint Application of SAR and Optical Images

by Keyu Ma, Kai Hu, Junyu Chen, Ming Jiang, Yao Xu, Min Xia and Liguo Weng

Remote Sens. 2025, 17(3), 505; https://doi.org/10.3390/rs17030505 - 31 Jan 2025

Abstract

The combined use of synthetic aperture radar (SAR) and optical images for surface observation is gaining increasing attention. Optical images, with their distinct edge features, can accurately classify different objects, while SAR images reveal deeper internal variations. To address the challenge of differing [...] Read more.

The combined use of synthetic aperture radar (SAR) and optical images for surface observation is gaining increasing attention. Optical images, with their distinct edge features, can accurately classify different objects, while SAR images reveal deeper internal variations. To address the challenge of differing feature distributions in multi-source images, we propose an edge enhancement network, OSNet (network for optical and SAR images), designed to jointly extract features from optical and SAR images and enhance edge feature representation. OSNet consists of three core modules: a dual-branch backbone, a synergistic attention integration module, and a global-guided local fusion module. These modules, respectively, handle modality-independent feature extraction, feature sharing, and global-local feature fusion. In the backbone module, we introduce a differentiable Lee filter and a Laplacian edge detection operator in the SAR branch to suppress noise and enhance edge features. Additionally, we designed a multi-source attention fusion module to facilitate cross-modal information exchange between the two branches. We validated OSNet’s performance on segmentation tasks (WHU-OPT-SAR) and regression tasks (SNOW-OPT-SAR). The results show that OSNet improved PA and MIoU by 2.31% and 2.58%, respectively, in the segmentation task, and reduced MAE and RMSE by 3.14% and 4.22%, respectively, in the regression task. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing II)

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