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Remote Sensing
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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (5th Edition)
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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (5th Edition)

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

Deadline for manuscript submissions: 15 November 2025 | Viewed by 2228

Special Issue Editors

Prof. Dr. Chunhui Zhao

E-Mail Website
Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; intelligent information processing
Special Issues, Collections and Topics in MDPI journals
Dr. Shou Feng

E-Mail Website
Guest Editor
College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jinchang Ren

E-Mail Website
Guest Editor
The National Subsea Centre, Robert Gordon University, Aberdeen AB21 0BH, UK
Interests: remote sensing; image processing; hyperspectral image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Dr. Hongsheng Zhang

E-Mail Website
Guest Editor
Department of Geography, The University of Hong Kong, Hong Kong 999077, China
Interests: coastal ecosystems and sustainability; coastal remote sensing; land use/land cover change; multi-sensor data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Wenjuan Zhang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; deep learning; image simulation and generation; image enhance

Special Issue Information

Dear Colleagues,

Remote sensing technology is an important technical means for human beings to perceive the world, and multimodal remote sensing technology has become the mainstream of current research. With the rapid development of artificial intelligence technology, many new remote sensing image processing methods and algorithms have been proposed. Moreover, rapid advances in remote sensing methods have also promoted the application of associated algorithms and techniques to problems in many related fields, such as classification, segmentation and clustering, target detection, etc. This Special Issue aims to report on and cover the latest advances and trends regarding artificial intelligence algorithms for remote sensing imagery processing. Papers focused on both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies to relevant scenarios of remote sensing images, are welcome to be submitted to this Special Issue. We look forward to receiving your contributions.

Prof. Dr. Chunhui Zhao
Dr. Shou Feng
Prof. Dr. Jinchang Ren
Dr. Hongsheng Zhang
Dr. Wenjuan Zhang
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

  • machine learning and deep learning for remote sensing
  • optical/multispectral/hyperspectral image processing
  • multi-modal remote sensing
  • LiDAR and SAR
  • wetland, ocean, and underwater remote sensing
  • target detection, anomaly detection, and change detection
  • semantic segmentation and classification
  • object reidentification using cross-domain/cross-dimensional images
  • object 3D modeling and mesh optimization
  • applications in remote sensing (urban, agriculture, etc.)

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Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 9871 KiB  
Article
AIR-POLSAR-CR1.0: A Benchmark Dataset for Cloud Removal in High-Resolution Optical Remote Sensing Images with Fully Polarized SAR
by Yuxi Wang, Wenjuan Zhang, Jie Pan, Wen Jiang, Fangyan Yuan, Bo Zhang, Xijuan Yue and Bing Zhang
Remote Sens. 2025, 17(2), 275; https://doi.org/10.3390/rs17020275 - 14 Jan 2025
Viewed by 779
Abstract
Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically [...] Read more.
Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (5th Edition))
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24 pages, 5004 KiB  
Article
SymSwin: Multi-Scale-Aware Super-Resolution of Remote Sensing Images Based on Swin Transformers
by Dian Jiao, Nan Su, Yiming Yan, Ying Liang, Shou Feng, Chunhui Zhao and Guangjun He
Remote Sens. 2024, 16(24), 4734; https://doi.org/10.3390/rs16244734 - 18 Dec 2024
Viewed by 984
Abstract
Despite the successful applications of the remote sensing image in agriculture, meteorology, and geography, its relatively low spatial resolution is hindering the further applications. Super-resolution technology is introduced to conquer such a dilemma. It is a challenging task due to the variations in [...] Read more.
Despite the successful applications of the remote sensing image in agriculture, meteorology, and geography, its relatively low spatial resolution is hindering the further applications. Super-resolution technology is introduced to conquer such a dilemma. It is a challenging task due to the variations in object size and textures in remote sensing images. To address that problem, we present SymSwin, a super-resolution model based on the Swin transformer aimed to capture a multi-scale context. The symmetric multi-scale window (SyMW) mechanism is proposed and integrated in the backbone, which is capable of perceiving features with various sizes. First, the SyMW mechanism is proposed to capture discriminative contextual features from multi-scale presentations using corresponding attentive window size. Subsequently, a cross-receptive field-adaptive attention (CRAA) module is introduced to model the relations among multi-scale contexts and to realize adaptive fusion. Furthermore, RS data exhibit poor spatial resolution, leading to insufficient visual information when merely spatial supervision is applied. Therefore, a U-shape wavelet transform (UWT) loss is proposed to facilitate the training process from the frequency domain. Extensive experiments demonstrate that our method achieves superior performance in both quantitative metrics and visual quality compared with existing algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (5th Edition))
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