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Artificial Intelligence Algorithm for Remote Sensing Imagery Processing (4th 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 September 2024 | Viewed by 1020

Special Issue Editors


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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

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Guest Editor
The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an 710071, China
Interests: image/video processing; coding and transmission; chip design; high-performance computing
Special Issues, Collections and Topics in MDPI journals
Department of Aerospace and Geodesy, Technical University of Munich, Lise-Meitner-Str. 9, 85521 Ottobrunn, Germany
Interests: volunteered geographic information; geospatial machine learning; multi-sensor data fusion; geo-semantics; remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
The State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: hyperspectral image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

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, and target detection, et al. This Special Issue aims to report and cover the latest advances and trends about the Artificial Intelligence Algorithm for Remote Sensing Imagery Processing. Papers on both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies relevant to scenarios of remote sensing images are welcome. We look forward to receiving your contributions.

Prof. Dr. Chunhui Zhao
Prof. Dr. Yunsong Li
Dr. Hao Li
Dr. Bobo Xi
Dr. Shou Feng
Dr. Nan Su
Dr. Yiming Yan
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
  • machine learning and deep learning for remote sensing
  • optical/multispectral/hyperspectral image processing
  • LiDAR and SAR
  • wetland, ocean and underwater remote sensing
  • target detection, anomaly detection, and change detection
  • semantic segmentation and classification
  • object re-identification using cross-domain/cross-dimensional images
  • object 3D modeling and mesh optimization
  • applications in remote sensing

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Published Papers (1 paper)

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20 pages, 4774 KiB  
Article
CSAN-UNet: Channel Spatial Attention Nested UNet for Infrared Small Target Detection
by Yuhan Zhong, Zhiguang Shi, Yan Zhang, Yong Zhang and Hanyu Li
Remote Sens. 2024, 16(11), 1894; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16111894 - 24 May 2024
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Abstract
Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the [...] Read more.
Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the nuances of small infrared target segmentation due to its reliance on downsampling operations, such as pooling, which often results in the loss of critical target information. This paper introduces the Channel Spatial Attention Nested UNet (CSAN-UNet), an innovative architecture designed specifically to enhance the detection and segmentation of small infrared targets. Central to CSAN-UNet’s design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priority and Spatial Attention Cascade Module (CPSAM) represents another pivotal advancement within CSAN-UNet, prioritizing channel-level adjustments alongside spatial attention mechanisms to efficiently extract deep semantic information pertinent to small infrared targets. Empirical validation conducted on two public datasets confirms that CSAN-UNet surpasses existing state-of-the-art algorithms in segmentation performance, while simultaneously reducing computational overhead. Full article
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