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Artificial Intelligence and Machine Learning with Applications in Remote Sensing II

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 13917

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Prof. Dr. Kuo-Chin Fan

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Department of Computer Science & Information Engineering, National Central University, Taoyuan 32001, Taiwan
Interests: remote sensing; artificial intelligence; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yang-Lang Chang

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Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: remote sensing; high performance computing; deep learning; pattern recognition; image processing
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Prof. Dr. Toshifumi Moriyama

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Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan
Interests: radar polarimetry; inverse scattering; microwave remote-sensing; wireless sensor networks
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Dr. Ying-Nong Chen

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Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
Interests: hyperspectral; multispectral signal processing; machine learning; deep learning; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, with the advancement of technology, there ever more data with higher spectral, spatial and temporal resolutions obtained from active and passive sensors. In addition, the applications of remote sensing data in environmental, commercial and military fields are becoming increasingly popular. This poses challenges in terms of effectively and efficiently processing large quantities of remote sensing data. In recent years, many useful feature mining deep learning, and decision tree-inspired algorithms for remote sensing data processing have drawn the attention of a large number of researchers and received unprecedented levels of attention. Even after so much research and such extensive algorithmic work have alreadu been devoted to this popular topic, there still remains so much for the community to study about artificial intelligence, machine learning and deep learning. Therefore, this Special Issue of Remote Sensing aims to demonstrate state-of-the-art studies on the use of artificial intelligence machine learning and deep learning algorithms for effective and efficient remote sensing applications. We invite papers in, but not limited to, the following areas:

Hyperspectral, multispectral applications with machine learning, deep learning algorithms
Remote sensing data processing based on artificial intelligence and machine learning
Hyperspectral, multispectral image processing
AI/deep learning/machine learning for big hyperspectral, multispectral data analysis
Remote sensing data for disasters, weather, water and climate applications based on AI/DL/ML algorithms
Deep learning-based transfer learning
Feature extraction with machine learning or deep learning for remote sensing data

This is the Second Edition of the Special Issue, and experts and scholars in related fields are welcome to submit their original works to this Special Issue.

https://www.mdpi.com/journal/remotesensing/special_issues/AI_ML_Applications

Prof. Dr. Kuo-Chin Fan
Prof. Dr. Yang-Lang Chang
Prof. Dr. Toshifumi Moriyama
Dr. Ying-Nong Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing data
artificial intelligence
machine learning
deep learning
hyperspectral images

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Related Special Issue

Artificial Intelligence and Machine Learning with Applications in Remote Sensing (Third Edition) in Remote Sensing

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Research

22 pages, 1329 KiB

Open AccessArticle

A Scene Classification Model Based on Global-Local Features and Attention in Lie Group Space

by Chengjun Xu, Jingqian Shu, Zhenghan Wang and Jialin Wang

Remote Sens. 2024, 16(13), 2323; https://doi.org/10.3390/rs16132323 - 25 Jun 2024

Cited by 1 | Viewed by 1145

Abstract

The efficient fusion of global and local multi-scale features is quite important for remote sensing scene classification (RSSC). The scenes in high-resolution remote sensing images (HRRSI) contain many complex backgrounds, intra-class diversity, and inter-class similarities. Many studies have shown that global features and local features are helpful for RSSC. The receptive field of a traditional convolution kernel is small and fixed, and it is difficult to capture global features in the scene. The self-attention mechanism proposed in transformer effectively alleviates the above shortcomings. However, such models lack local inductive bias, and the calculation is complicated due to the large number of parameters. To address these problems, in this study, we propose a classification model of global-local features and attention based on Lie Group space. The model is mainly composed of three independent branches, which can effectively extract multi-scale features of the scene and fuse the above features through a fusion module. Channel attention and spatial attention are designed in the fusion module, which can effectively enhance the crucial features in the crucial regions, to improve the accuracy of scene classification. The advantage of our model is that it extracts richer features, and the global-local features of the scene can be effectively extracted at different scales. Our proposed model has been verified on publicly available and challenging datasets, taking the AID as an example, the classification accuracy reached 97.31%, and the number of parameters is 12.216 M. Compared with other state-of-the-art models, it has certain advantages in terms of classification accuracy and number of parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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24 pages, 4053 KiB

Open AccessArticle

Hyperspectral Image Denoising Based on Deep and Total Variation Priors

by Peng Wang, Tianman Sun, Yiming Chen, Lihua Ge, Xiaoyi Wang and Liguo Wang

Remote Sens. 2024, 16(12), 2071; https://doi.org/10.3390/rs16122071 - 7 Jun 2024

Cited by 1 | Viewed by 923

Abstract

To address the problems of noise interference and image blurring in hyperspectral imaging (HSI), this paper proposes a denoising method for HSI based on deep learning and a total variation (TV) prior. The method minimizes the first-order moment distance between the deep prior of a Fast and Flexible Denoising Convolutional Neural Network (FFDNet) and the Enhanced 3D TV (E3DTV) prior, obtaining dual priors that complement and reinforce each other’s advantages. Specifically, the original HSI is initially processed with a random binary sparse observation matrix to achieve a sparse representation. Subsequently, the plug-and-play (PnP) algorithm is employed within the framework of generalized alternating projection (GAP) to denoise the sparsely represented HSI. Experimental results demonstrate that, compared to existing methods, this method shows significant advantages in both quantitative and qualitative assessments, effectively enhancing the quality of HSIs. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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25 pages, 6558 KiB

Open AccessArticle

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

by Ilham Adi Panuntun, Ilham Jamaluddin, Ying-Nong Chen, Shiou-Nu Lai and Kuo-Chin Fan

Remote Sens. 2024, 16(6), 1078; https://doi.org/10.3390/rs16061078 - 19 Mar 2024

Cited by 2 | Viewed by 3305

Abstract

Mangroves grow in intertidal zones in tropical and subtropical regions, offering numerous advantages to humans and ecosystems. Mangrove monitoring is one of the important tasks to understand the current status of mangrove forests regarding their loss issues, including deforestation and degradation. Currently, satellite imagery is widely employed to monitor mangrove ecosystems. Sentinel-2 is an optical satellite imagery whose data are available for free, and which provides satellite imagery at a 5-day temporal resolution. Analyzing satellite images before and after loss can enhance our ability to detect mangrove loss. This paper introduces a LSST-Former model that considers the situation before and after mangrove loss to categorize non-mangrove areas, intact mangroves, and mangrove loss categories using Sentinel-2 images for a limited number of labels. The LSST-Former model was developed by integrating a fully convolutional network (FCN) and a transformer base with few-shot learning algorithms to extract information from spectral-spatial-temporal Sentinel-2 images. The attention mechanism in the transformer algorithm may effectively mitigate the issue of limited labeled samples and enhance the accuracy of learning correlations between samples, resulting in more successful classification. The experimental findings demonstrate that the LSST-Former model achieves an overall accuracy of 99.59% and an Intersection-over-Union (IoU) score of 98.84% for detecting mangrove loss, and the validation of universal applicability achieves an overall accuracy of more than 92% and a kappa accuracy of more than 89%. LSST-Former demonstrates superior performance compared to state-of-the-art deep-learning models such as random forest, Support Vector Machine, U-Net, LinkNet, Vision Transformer, SpectralFormer, MDPrePost-Net, and SST-Former, as evidenced by the experimental results and accuracy metrics. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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20 pages, 7569 KiB

Open AccessArticle

Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements

by Wei Liu, He Wang, Zhenzhu Xi and Liang Wang

Remote Sens. 2024, 16(1), 62; https://doi.org/10.3390/rs16010062 - 22 Dec 2023

Cited by 2 | Viewed by 1539

Abstract

Despite demonstrating exceptional inversion production for synthetic data, the application of deep learning (DL) inversion methods to invert realistic magnetotelluric (MT) measurements, which are inevitably contaminated by noise in acquisition, poses a significant challenge. Hence, to facilitate DL inversion for realistic MT measurements, this work explores developing a noise-robust MT DL inversion method by generating targeted noisy training datasets and constructing a physics-informed neural network. Different from most previous works that only considered the noise of one fixed distribution and level, we propose three noise injection strategies and compare their combinations to mitigate the adverse effect of measurement noise on MT DL inversion results: (1) add synthetic relative noise obeying Gaussian distribution; (2) propose a multiwindow Savitzky–Golay (MWSG) filtering scheme to extract potential and possible noise from the target field data and then introduce them into training data; (3) create an augmented training dataset based on the former two strategies. Moreover, we employ the powerful Swin Transformer as the backbone network to construct a U-shaped DL model (SwinTUNet), based on which a physics-informed SwinTUNet (PISwinTUNet) is implemented to further enhance its generalization ability. In synthetic examples, the proposed noise injection strategies demonstrate impressive inversion effects, regardless of whether they are contaminated by familiar or unfamiliar noise. In a field example, the combination of three strategies drives PISwinTUNet to produce considerably faithful reconstructions for subsurface resistivity structures and outperform the classical deterministic Occam inversions. The experimental results show that the proposed noise-robust DL inversion method based on the noise injection strategies and physics-informed DL architecture holds great promise in processing MT field data. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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17 pages, 3396 KiB

Open AccessArticle

Sofia Airport Visibility Estimation with Two Machine-Learning Techniques

by Nikolay Penov and Guergana Guerova

Remote Sens. 2023, 15(19), 4799; https://doi.org/10.3390/rs15194799 - 1 Oct 2023

Cited by 3 | Viewed by 1773

Abstract

Fog is a weather phenomenon with visibility below 1 km. Fog heavily influences ground and air traffic, leading to accidents and delays. The main goal of this study is to use two machine-learning (ML) techniques—the random forest (RF) and long short-term memory (LSTM) models—to estimate visibility using 11 meteorological parameters. Several meteorological elements related to fog are investigated, including pressure, temperature, wind speed, and direction. The seasonal cycle shows that fog in Sofia has a peak in winter, but a small secondary peak in spring was found in this study. Fog occurrence has a tendency to decrease during the studied period, with the peak of fog observations being shifted towards the higher visibility range. The input parameters in the models are day of year, hour, wind speed, wind direction, first-cloud-layer coverage, first-cloud-layer base height, temperature, dew point, dew-point deficit, pressure, and fog stability index (FSI). The FSI and dew-point deficit are evaluated as the most important input parameters by the RF model. Post-processing was performed with double linear regression for the correction of the predictions by the models, which led to a significant improvement in performance. Both models were found to describe the complexity of fog well. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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27 pages, 4463 KiB

Open AccessArticle

A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression

by Haixia Zhao, You Zhou, Tingting Bai and Yuanzhong Chen

Remote Sens. 2023, 15(18), 4569; https://doi.org/10.3390/rs15184569 - 17 Sep 2023

Cited by 3 | Viewed by 1874

Abstract

Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-Unet. The MSDC-Unet mainly contains modules of deformable convolution and dilated convolution. The deformable convolution can change the shape of the convolution kernel to adjust the shape of seismic signals to fit different features, while the dilated convolution with different dilation rates is used to extract feature information at different scales. Furthermore, we combine Charbonnier loss and structure similarity index measure (SSIM) to better characterize geological structures of seismic data. Several examples of synthetic and field seismic data demonstrate that the proposed method is effective in the comprehensive results in terms of quantitative metrics and visual effect of denoising, compared with two traditional denoising methods and two deep convolutional neural network denoising models. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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22 pages, 16773 KiB

Open AccessArticle

Neural-Network-Based Target Classification and Range Detection by CW MMW Radar

by Yair Richter, Nezah Balal and Yosef Pinhasi

Remote Sens. 2023, 15(18), 4553; https://doi.org/10.3390/rs15184553 - 15 Sep 2023

Cited by 3 | Viewed by 1216

Abstract

This study presents a reliable classification of walking pedestrians and animals using a radar operating in the millimeter waves (mmW) regime. In addition to the defined targets, additional targets were added in an attempt to fool the radar and to present the robustness of the proposed technique. In addition to the classification capabilities, the presented scheme allowed for the ability to detect the range of targets. The classification was achieved using a deep neural network (DNN) architecture, which received the recordings from the radar as an input after the pre-processing procedure. Qualitative detection was made possible due to the radar’s operation at extremely high frequencies so that even the tiny movements of limbs influenced the detection, thus enabling the high-quality classification of various targets. The classification results presented a high achievable accuracy even in the case where the targets attempted to fool the radar and mimicked other targets. The combination of the use of high frequencies alongside neural-network-based classification demonstrated the superiority of the proposed scheme in this research over the state of the art. The neural network was analyzed with the help of interpretable tools such as explainable AI (XAI) to achieve a better understanding of the DNN’s decision-making process and the mechanisms via which it was able to perform multiple tasks at once. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine Learning with Applications in Remote Sensing II)

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