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Special Issue "Advanced Machine Learning and Deep Learning Approaches for Remote Sensing III"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 2810

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Special Issue Editor

Dr. Gwanggil Jeon

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Guest Editor

Department of Embedded Systems Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
Interests: remote sensing; deep learning; artificial intelligence; image processing; signal processing
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Special Issue Information

Dear Colleagues,

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact. Artificial intelligence such as machine learning and deep learning has shown potential to overcome the challenges of remote sensing signal, image, and video processing. Artificial intelligence approaches require huge computing power as they normally use GPUs. As a result of research efforts, recent advances in remote sensing have led to high-resolution monitoring of Earth on a global scale, providing a massive amount of Earth-observation data. We trust that artificial intelligence, machine learning, and deep learning approaches will provide promising tools to overcome many challenges in remote sensing in terms of accuracy and reliability at high speeds.

This Special Issue is the third edition of “Advanced Machine Learning for Time Series Remote Sensing Data Analysis”. In this third edition, our new Special Issue aims to introduce the latest advances and trends concerning advanced machine learning and deep learning techniques in relation to remote sensing data-processing issues. Papers of both theoretical and applicative nature, as well as contributions regarding new advanced artificial learning and data science techniques for the remote sensing research community, are welcome.

Both original research articles and review articles are welcome for submission.

This Special Issue is the third edition of the following Special Issues: “Advanced Machine Learning and Deep Learning Approaches for Remote Sensing and Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II”.

Dr. Gwanggil Jeon
Guest Editor

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

machine learning
remote sensing
signal/image processing
deep learning
artificial intelligence
time series processing

Related Special Issue

Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II in Remote Sensing (16 articles)

Published Papers (4 papers)

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Research

23 pages, 65467 KiB

Open AccessArticle

FAUNet: Frequency Attention U-Net for Parcel Boundary Delineation in Satellite Images

Bahaa Awad

and

Isin Erer

Remote Sens. 2023, 15(21), 5123; https://doi.org/10.3390/rs15215123 - 26 Oct 2023

Viewed by 572

Abstract

Parcel detection and boundary delineation play an important role in numerous remote sensing applications, such as yield estimation, crop type classification, and farmland management systems. Consequently, achieving accurate boundary delineation remains a prominent research area within remote sensing literature. In this study, we propose a straightforward yet highly effective method for boundary delineation that leverages frequency attention to enhance the precision of boundary detection. Our approach, named Frequency Attention U-Net (FAUNet), builds upon the foundational and successful U-Net architecture by incorporating a frequency-based attention gate to enhance edge detection performance. Unlike many similar boundary delineation methods that employ three segmentation masks, our network employs only two, resulting in a more streamlined post-processing workflow. The essence of frequency attention lies in the integration of a frequency gate utilizing a high-pass filter. This high-pass filter output accentuates the critical high-frequency components within feature maps, thereby significantly improves edge detection performance. Comparative evaluation of FAUNet against alternative models demonstrates its superiority across various pixel-based and object-based metrics. Notably, FAUNet achieves a pixel-based precision, F1 score, and IoU of 0.9047, 0.8692, and 0.7739, respectively. In terms of object-based metrics, FAUNet demonstrates minimal over-segmentation (OS) and under-segmentation (US) errors, with values of 0.0341 and 0.1390, respectively. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Deep Learning Approaches for Remote Sensing III)

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26 pages, 41363 KiB

Open AccessArticle

Spectral Segmentation Multi-Scale Feature Extraction Residual Networks for Hyperspectral Image Classification

and

Remote Sens. 2023, 15(17), 4219; https://doi.org/10.3390/rs15174219 - 28 Aug 2023

Viewed by 586

Abstract

Hyperspectral image (HSI) classification is a vital task in hyperspectral image processing and applications. Convolutional neural networks (CNN) are becoming an effective approach for categorizing hyperspectral remote sensing images as deep learning technology advances. However, traditional CNN usually uses a fixed kernel size, which limits the model’s capacity to acquire new features and affects the classification accuracy. Based on this, we developed a spectral segmentation-based multi-scale spatial feature extraction residual network (MFERN) for hyperspectral image classification. MFERN divides the input data into many non-overlapping sub-bands by spectral bands, extracts features in parallel using the multi-scale spatial feature extraction module MSFE, and adds global branches on top of this to obtain global information of the full spectral band of the image. Finally, the extracted features are fused and sent into the classifier. Our MSFE module has multiple branches with increasing ranges of the receptive field (RF), enabling multi-scale spatial information extraction at both fine- and coarse-grained levels. On the Indian Pines (IP), Salinas (SA), and Pavia University (PU) HSI datasets, we conducted extensive experiments. The experimental results show that our model has the best performance and robustness, and our proposed MFERN significantly outperforms other models in terms of classification accuracy, even with a small amount of training data. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Deep Learning Approaches for Remote Sensing III)

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24 pages, 6952 KiB

Open AccessArticle

A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments

Jingwei Xiong

Jifei Pan

and

Mingyang Du

Remote Sens. 2023, 15(16), 4083; https://doi.org/10.3390/rs15164083 - 19 Aug 2023

Viewed by 423

Abstract

Target recognition mainly focuses on three approaches: optical-image-based, echo-detection-based, and passive signal-analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an “end-to-end” model that cascades a noise estimation network with a recognition network to identify working modes in noisy environments. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signals, the recognition network is realized based on the multi-scale convolutional attention network (MSCANet). Firstly, deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, the finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem of the conventional method being seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environment, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, the proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Deep Learning Approaches for Remote Sensing III)

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18 pages, 2126 KiB

Open AccessArticle

DAFNet: A Novel Change-Detection Model for High-Resolution Remote-Sensing Imagery Based on Feature Difference and Attention Mechanism

and

Remote Sens. 2023, 15(15), 3896; https://doi.org/10.3390/rs15153896 - 06 Aug 2023

Viewed by 843

Abstract

Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on feature differences and attention mechanisms. This network includes a Siamese architecture-encoding network that encodes images at different times, a Difference Feature-Extraction Module (DFEM) for extracting difference features from bitemporal images, an Attention-Regulation Module (ARM) for optimizing the extracted difference features through attention, and a Cross-Scale Feature-Fusion Module (CSFM) for merging features from different encoding stages. Experimental results demonstrate that this method effectively alleviates issues of target misdetection, false alarms, and blurry edges. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Deep Learning Approaches for Remote Sensing III)

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