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Intelligent Image Analysis: Advancing Remote Sensing with Artificial Intelligence

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

Deadline for manuscript submissions: 29 October 2025 | Viewed by 345

Special Issue Editors


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Guest Editor
School of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: quantum-inspired evolutionary computation; computation intelligence; machine learning; pattern recognition

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Guest Editor
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
Interests: remote sensing image captioning

Special Issue Information

Dear Colleagues,

The rapid evolution of remote sensing technologies has enabled daily acquisition of massive remote sensing images (RSIs). However, the exponential expansion of RSIs in terms of volume, diversity, and complexity has fundamentally challenged the conventional image analysis methods reliant on manually crafted features and shallow machine learning architectures. In recent years, artificial intelligence has exhibited revolutionary breakthroughs and provides a transformative solution for RSI analysis. Researchers have significantly enhanced the accuracy and efficiency of RSI analysis by leveraging advanced AI techniques. Notably, the emergence of remote sensing foundation models has driven substantial progress in cognitive reasoning capabilities for remote sensing applications.

Therefore, we have organized a Special Issue titled “Intelligent Image Analysis: Advancing Remote Sensing with Artificial Intelligence” in Remote Sensing. This Special Issue aspires to create a platform to share and discuss studies on advanced AI technology for RSI analysis. The research topics cover classification, detection, segmentation, and other image analysis tasks, and the data sources include, but are not limited to, optical, hyperspectral, and SAR. We welcome submissions relevant to intelligent RSI interpretation, remote sensing foundation models, multi-modal RSI analysis, remote sensing 3D reconstruction, open-world RSI interpretation, and other remote sensing interpretation applications with advanced AI technology.

Prof. Dr. Yangyang Li
Dr. Tianyang Zhang
Dr. Xinghua Li
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

  • intelligent RSI interpretation
  • remote sensing foundation models development and downstream applications
  • multi-modal RSI interpretation
  • remote sensing 3D reconstruction
  • open-world remote sensing image interpretation
  • remote sensing interpretation applications with advanced AI technology

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Published Papers (1 paper)

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Research

24 pages, 19550 KiB  
Article
TMTS: A Physics-Based Turbulence Mitigation Network Guided by Turbulence Signatures for Satellite Video
by Jie Yin, Tao Sun, Xiao Zhang, Guorong Zhang, Xue Wan and Jianjun He
Remote Sens. 2025, 17(14), 2422; https://doi.org/10.3390/rs17142422 - 12 Jul 2025
Viewed by 201
Abstract
Atmospheric turbulence severely degrades high-resolution satellite videos through spatiotemporally coupled distortions, including temporal jitter, spatial-variant blur, deformation, and scintillation, thereby constraining downstream analytical capabilities. Restoring turbulence-corrupted videos poses a challenging ill-posed inverse problem due to the inherent randomness of turbulent fluctuations. While existing [...] Read more.
Atmospheric turbulence severely degrades high-resolution satellite videos through spatiotemporally coupled distortions, including temporal jitter, spatial-variant blur, deformation, and scintillation, thereby constraining downstream analytical capabilities. Restoring turbulence-corrupted videos poses a challenging ill-posed inverse problem due to the inherent randomness of turbulent fluctuations. While existing turbulence mitigation methods for long-range imaging demonstrate partial success, they exhibit limited generalizability and interpretability in large-scale satellite scenarios. Inspired by refractive-index structure constant (Cn2) estimation from degraded sequences, we propose a physics-informed turbulence signature (TS) prior that explicitly captures spatiotemporal distortion patterns to enhance model transparency. Integrating this prior into a lucky imaging framework, we develop a Physics-Based Turbulence Mitigation Network guided by Turbulence Signature (TMTS) to disentangle atmospheric disturbances from satellite videos. The framework employs deformable attention modules guided by turbulence signatures to correct geometric distortions, iterative gated mechanisms for temporal alignment stability, and adaptive multi-frame aggregation to address spatially varying blur. Comprehensive experiments on synthetic and real-world turbulence-degraded satellite videos demonstrate TMTS’s superiority, achieving 0.27 dB PSNR and 0.0015 SSIM improvements over the DATUM baseline while maintaining practical computational efficiency. By bridging turbulence physics with deep learning, our approach provides both performance enhancements and interpretable restoration mechanisms, offering a viable solution for operational satellite video processing under atmospheric disturbances. Full article
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