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Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition
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Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition

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 2024 | Viewed by 2793

Special Issue Editors

Prof. Dr. Dong Ren

E-Mail Website
Guest Editor
College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Interests: unsupervised domain adaptation; vegetation segmentation; mixed target domain; incremental learning
Dr. Huiqin Ma

E-Mail Website
Guest Editor
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: remote sensing; machine learning
Dr. Hang Sun

E-Mail Website
Guest Editor
College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Interests: processing of remote sensing image data; remote sensing image quality enhancement and restoration
Dr. Li Liu

E-Mail Website
Guest Editor
College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China
Interests: meteorological disaster monitoring of paddy rice; processing of remote sensing image data

Special Issue Information

Dear Colleagues,

With the rapid accumulation of high-resolution remote sensing data and internet geographic information, the demand for intelligent processing and analysis of remote sensing big data is increasing day by day. Artificial intelligence (AI) technology provides strong support for improving the efficiency and accuracy of remote sensing data acquisition, and brings new possibilities for the processing, analysis and application of remote sensing data. From image recognition to land cover classification, and from change detection to environmental monitoring, AI demonstrates extensive and far-reaching applications in the field of remote sensing.

However, AI remote sensing still faces numerous challenges, such as acquiring annotated data, algorithm interpretability, and fully utilizing spatial and multi-dimensional features of remote sensing data, which require further breakthroughs. Moreover, amidst the plethora of publications in both remote sensing and AI fields, researchers in interdisciplinary areas need to expend considerable effort to review and keep up with the pace of developments. This Special Issue aims to explore the latest advances, challenges and application prospects of AI in the field of remote sensing. The topics may cover new theories, methods and applications of AI technology in remote sensing data processing, remote sensing image analysis and remote sensing applications.

Articles may address, but are not limited to, the following topics:

  • Remote sensing monitoring of major crop pests and diseases based on artificial intelligence technology;
  • Application of deep learning in spatio-temporal big data for forest monitoring;
  • Deep learning for crop classification with remote sensing data;
  • Application of artificial intelligence and remote sensing in frontier research on agricultural meteorological disasters;
  • Environmental remote sensing monitoring with artificial intelligence technology;
  • Application of artificial intelligence in change detection and monitoring;
  • Hyperspectral/multispectral/optical remote sensing image quality enhancement and restoration with artificial intelligence technology;
  • Remote sensing image interpretation with artificial intelligence technology.

Prof. Dr. Dong Ren
Dr. Huiqin Ma
Dr. Hang Sun
Dr. Li Liu
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
  • deep learning
  • remote sensing image interpretation
  • object detection
  • change detection
  • data fusion
  • environmental monitoring
  • remote sensing image quality enhancement and restoration

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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22 pages, 16111 KiB  
Article
LULC-SegNet: Enhancing Land Use and Land Cover Semantic Segmentation with Denoising Diffusion Feature Fusion
by Zongwen Shi, Junfu Fan, Yujie Du, Yuke Zhou and Yi Zhang
Remote Sens. 2024, 16(23), 4573; https://doi.org/10.3390/rs16234573 - 6 Dec 2024
Viewed by 313
Abstract
Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates [...] Read more.
Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). This network enhances the clarity of the edge segmentation, detail resolution, and the visualization and accuracy of the contours by delving into the spatial details of the remote sensing images. The LULC-SegNet incorporates DDPM decoder features into the LULC segmentation task, utilizing machine learning clustering algorithms and spatial attention to extract continuous DDPM semantic features. The network addresses the potential loss of spatial details during feature extraction in convolutional neural network (CNN), and the integration of the DDPM features with the CNN feature extraction network improves the accuracy of the segmentation boundaries of the geographical features. Ablation and comparison experiments conducted on the Circum-Tarim Basin Region LULC Dataset demonstrate that the LULC-SegNet improved the LULC semantic segmentation. The LULC-SegNet excels in multiple key performance indicators compared to existing advanced semantic segmentation methods. Specifically, the network achieved remarkable scores of 80.25% in the mean intersection over union (MIOU) and 93.92% in the F1 score, surpassing current technologies. The LULC-SegNet demonstrated an IOU score of 73.67%, particularly in segmenting the small-sample river class. Our method adapts to the complex geophysical characteristics of remote sensing datasets, enhancing the performance of automatic semantic segmentation tasks for land use and land cover changes and making critical advancements. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition)
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22 pages, 4161 KiB  
Article
A Multi-Tiered Collaborative Network for Optical Remote Sensing Fine-Grained Ship Detection in Foggy Conditions
by Wenbo Zhou, Ligang Li, Bo Liu, Yuan Cao and Wei Ni
Remote Sens. 2024, 16(21), 3968; https://doi.org/10.3390/rs16213968 - 25 Oct 2024
Viewed by 722
Abstract
Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency [...] Read more.
Ship target detection faces the challenges of complex and changing environments combined with the varied characteristics of ship targets. In practical applications, the complexity of meteorological conditions, uncertainty of lighting, and the diversity of ship target characteristics can affect the accuracy and efficiency of ship target detection algorithms. Most existing target detection methods perform well in conditions of a general scenario but underperform in complex conditions. In this study, a collaborative network for target detection under foggy weather conditions is proposed, aiming to achieve improved accuracy while satisfying the need for real-time detection. First, a collaborative block was designed and SCConv and PCA modules were introduced to enhance the detection of low-quality images. Second, the PAN + FPN structure was adopted to take full advantage of its lightweight and efficient features. Finally, four detection heads were used to enhance the performance. In addition to this, a dataset for foggy ship detection was constructed based on ShipRSImageNet, and the mAP on the dataset reached 48.7%. The detection speed reached 33.3 frames per second (FPS), which is ultimately comparable to YOLOF. It shows that the model proposed has good detection effectiveness for remote sensing ship images during low-contrast foggy days. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition)
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23 pages, 5761 KiB  
Article
FFA: Foreground Feature Approximation Digitally against Remote Sensing Object Detection
by Rui Zhu, Shiping Ma, Linyuan He and Wei Ge
Remote Sens. 2024, 16(17), 3194; https://doi.org/10.3390/rs16173194 - 29 Aug 2024
Viewed by 737
Abstract
In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) [...] Read more.
In recent years, research on adversarial attack techniques for remote sensing object detection (RSOD) has made great progress. Still, most of the research nowadays is on end-to-end attacks, which mainly design adversarial perturbations based on the prediction information of the object detectors (ODs) to achieve the attack. These methods do not discover the common vulnerabilities of the ODs and, thus, the transferability is weak. Based on this, this paper proposes a foreground feature approximation (FFA) method to generate adversarial examples (AEs) that discover the common vulnerabilities of the ODs by changing the feature information carried by the image itself to implement the attack. Specifically, firstly, the high-quality predictions are filtered as attacked objects using the detector, after which a hybrid image without any target is made, and the hybrid foreground is created based on the attacked targets. The images’ shallow features are extracted using the backbone network, and the features of the input foreground are approximated towards the hybrid foreground to implement the attack. In contrast, the model predictions are used to assist in realizing the attack. In addition, we have found the effectiveness of FFA for targeted attacks, and replacing the hybrid foreground with the targeted foreground can realize targeted attacks. Extensive experiments are conducted on the remote sensing target detection datasets DOTA and UCAS-AOD with seven rotating target detectors. The results show that the mAP of FFA under the IoU threshold of 0.5 untargeted attack is 3.4% lower than that of the advanced method, and the mAP of FFA under targeted attack is 1.9% lower than that of the advanced process. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition)
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17 pages, 3992 KiB  
Technical Note
Auroral Image Classification Based on Second-Order Convolutional Network and Channel Attention Awareness
by Yangfan Hu, Zeming Zhou, Pinglv Yang, Xiaofeng Zhao, Qian Li and Peng Zhang
Remote Sens. 2024, 16(17), 3178; https://doi.org/10.3390/rs16173178 - 28 Aug 2024
Viewed by 475
Abstract
Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a [...] Read more.
Accurate classification of ground-based auroral images is essential for studying variations in auroral morphology and uncovering magnetospheric mechanisms. However, distinguishing subtle morphological differences among different categories of auroral images presents a significant challenge. To excavate more discriminative information from ground-based auroral images, a novel method named learning representative channel attention information from second-order statistics (LRCAISS) is proposed. The LRCAISS is highlighted with two innovative techniques: a second-order convolutional network and a novel second-order channel attention block. The LRCAISS extends from Resnet50 architecture by incorporating a second-order convolutional network to capture more detailed statistical representation. Meanwhile, the novel second-order channel attention block effectively recalibrates these features. LACAISS is evaluated on two public ground-based auroral image datasets, and the experimental results demonstrate that LRCAISS achieves competitive performance compared to existing methods. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition)
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