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Remote Sensing
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Recent Progress on Remote Sensing Change Detection and Application Driven...
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Recent Progress on Remote Sensing Change Detection and Application Driven by AI

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

Deadline for manuscript submissions: 16 July 2024 | Viewed by 5877

Special Issue Editors

Dr. Xinzheng Zhang

E-Mail Website
Guest Editor
School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Interests: remote sensing; intelligent information processing; artificial intelligence
Dr. Ce Zhang

E-Mail Website
Guest Editor
School of Geographical Sciences, University of Bristol, Bristol BS8 1QU, UK
Interests: machine learning; deep learning; geospatial data science; remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hong Huang

E-Mail Website
Guest Editor
The College of Ophotoelectric Engineering, Chongqing University, Chongqing 400044, China
Interests: remote sensing; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Change detection based on remote sensing data plays an important role on a wide range of applications, such as environmental monitoring, urban planning, agriculture investigation, disaster assessment. With the development of numerous and various Earth observation sensors, remote sensing enters the era of “Big Data”, which presents both a challenge and an opportunity for change detection. One urgent issue to be solved is how to extract and excavate the meaningful information in the large amount of unlabeled remote sensing data acquired from different sources. Apart from this, many new problems have emerged in the field. This calls for innovative theories and technologies to overcome these challenges.

In recent years, artificial intelligence (AI) technology has become a research focus in developing new change detection methods. These techniques driven by AI can benefit feature representation and help improve experts’ understanding of remote sensing big data.

With this context, this special issue aims to present articles that focus primarily on change detection and its application based on advanced AI across different sensors and platforms. The special issue welcomes articles concerning novel approaches or case studies to the study of remote sensing change detection. Topics can be related but not limited to:

  • Change detection using heterogeneous remote sensing imagery
  • Polarimetric SAR change detection
  • Hyperspectral remote sensing change detection
  • Weak and Small area change detection
  • Self-supervised learning for change detection
  • Adversarial learning for change detection
  • Explainable AI for change detection
  • Reliability AI for change detection

Dr. Xinzheng Zhang
Dr. Ce Zhang
Prof. Dr. Hong Huang
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
  • change detection
  • artificial intelligence
  • deep learning
  • neural network
  • image processing

Published Papers (4 papers)

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Research

18 pages, 3397 KiB  
Article
A Heterogeneity-Enhancement and Homogeneity-Restraint Network (HEHRNet) for Change Detection from Very High-Resolution Remote Sensing Imagery
by Biao Wang, Ao He, Chunlin Wang, Xiao Xu, Hui Yang and Yanlan Wu
Remote Sens. 2023, 15(22), 5425; https://doi.org/10.3390/rs15225425 - 20 Nov 2023
Viewed by 861
Abstract
Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, [...] Read more.
Change detection (CD), a crucial technique for observing ground-level changes over time, is a challenging research area in the remote sensing field. Deep learning methods for CD have made significant progress in remote sensing intelligent interpretation. However, with very high-resolution (VHR) satellite imagery, technical challenges such as insufficient mining of shallow-level features, complex transmission of deep-level features, and difficulties in identifying change information features have led to severe fragmentation and low completeness issues of CD targets. To reduce costs and enhance efficiency in monitoring tasks such as changes in national resources, it is crucial to promote the practical implementation of automatic change detection technology. Therefore, we propose a deep learning approach utilizing heterogeneity enhancement and homogeneity restraint for CD. In addition to comprehensively extracting multilevel features from multitemporal images, we introduce a cosine similarity-based module and a module for progressive fusion enhancement of multilevel features to enhance deep feature extraction and the change information utilization within feature associations. This ensures that the change target completeness and the independence between change targets can be further improved. Comparative experiments with six CD models on two benchmark datasets demonstrate that the proposed approach outperforms conventional CD models in various metrics, including recall (0.6868, 0.6756), precision (0.7050, 0.7570), F1 score (0.6958, 0.7140), and MIoU (0.7013, 0.7000), on the SECOND and the HRSCD datasets, respectively. According to the core principles of change detection, the proposed deep learning network effectively enhances the completeness of target vectors and the separation of individual targets in change detection with VHR remote sensing images, which has significant research and practical value. Full article
(This article belongs to the Special Issue Recent Progress on Remote Sensing Change Detection and Application Driven by AI)
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20 pages, 6483 KiB  
Article
Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid
by Xinzheng Zhang, Dong Hu, Sheng Li, Yuqing Luo, Jinlin Li and Ce Zhang
Remote Sens. 2023, 15(18), 4480; https://doi.org/10.3390/rs15184480 - 12 Sep 2023
Cited by 1 | Viewed by 1000
Abstract
Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To [...] Read more.
Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks. Full article
(This article belongs to the Special Issue Recent Progress on Remote Sensing Change Detection and Application Driven by AI)
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21 pages, 2095 KiB  
Article
Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture
by Hongyang Yin, Chong Ma, Liguo Weng, Min Xia and Haifeng Lin
Remote Sens. 2023, 15(17), 4186; https://doi.org/10.3390/rs15174186 - 25 Aug 2023
Cited by 2 | Viewed by 1230
Abstract
Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous [...] Read more.
Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous predictions might accumulate in the subsequent blocks, leading to incomplete change detection results in terms of structure. To address this issue, we propose a bitemporal remote sensing image change detection network based on a Siamese-attention feedback architecture, referred to as SAFNet. First, we propose a global semantic module (GSM) on the encoder network, aiming to generate a low-resolution semantic change map to capture the changed objects. Second, we introduce a temporal interaction module (TIM), which is built through each encoding and decoding block, using the feature feedback between two temporal blocks to enhance the network’s perception ability of the entire changed target. Finally, we propose two auxiliary modules—the change feature extraction module (CFEM) and the feature refinement module (FRM)—which are further used to learn the fine boundaries of the changed target. The deep model we propose produced satisfying results in dual-temporal remote sensing image change detection. Extensive experiments on two remote sensing image change detection datasets demonstrate that the SAFNet algorithm exhibits state-of-the-art performance. Full article
(This article belongs to the Special Issue Recent Progress on Remote Sensing Change Detection and Application Driven by AI)
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19 pages, 5618 KiB  
Article
A Lightweight Dual-Branch Swin Transformer for Remote Sensing Scene Classification
by Fujian Zheng, Shuai Lin, Wei Zhou and Hong Huang
Remote Sens. 2023, 15(11), 2865; https://doi.org/10.3390/rs15112865 - 31 May 2023
Cited by 5 | Viewed by 1963
Abstract
The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still [...] Read more.
The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-resolution remote sensing images, the connectivity between neighboring windows is still limited. Meanwhile, ViT-based methods commonly contain a large number of parameters, resulting in a huge computational consumption. In this paper, a novel lightweight dual-branch swin transformer (LDBST) method for remote sensing scene classification is proposed, and the discriminative ability of scene features is increased through combining a ViT branch and convolutional neural network (CNN) branch. First, based on the hierarchical swin transformer model, LDBST divides the input features of each stage into two parts, which are then separately fed into the two branches. For the ViT branch, a dual multilayer perceptron structure with a depthwise convolutional layer, termed Conv-MLP, is integrated into the branch to boost the connections with neighboring windows. Then, a simple-structured CNN branch with maximum pooling preserves the strong features of the scene feature map. Specifically, the CNN branch lightens the LDBST, by avoiding complex multi-head attention and multilayer perceptron computations. To obtain better feature representation, LDBST was pretrained on the large-scale remote scene classification images of the MLRSN and RSD46-WHU datasets. These two pretrained weights were fine-tuned on target scene classification datasets. The experimental results showed that the proposed LDBST method was more effective than some other advanced remote sensing scene classification methods. Full article
(This article belongs to the Special Issue Recent Progress on Remote Sensing Change Detection and Application Driven by AI)
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