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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 (2nd Edition)

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 2026 | Viewed by 1769

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
Special Issues, Collections and Topics in MDPI journals
Dr. Huiqin Ma

E-Mail Website
Guest Editor
School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
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
Special Issues, Collections and Topics in MDPI journals
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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid accumulation of high-resolution remote sensing data and internet-based 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 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 the spatial and multi-dimensional features of remote sensing data, thus requiring further breakthroughs. Moreover, amidst the plethora of publications in both remote sensing and AI fields, significant effort from researchers in interdisciplinary areas is needed 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. 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;
  • The application of deep learning in spatio-temporal big data for forest monitoring;
  • Deep learning for crop classification with remote sensing data;
  • The application of artificial intelligence and remote sensing in frontier research on agricultural meteorological disasters;
  • Environmental remote sensing monitoring with artificial intelligence technology;
  • The 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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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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Related Special Issue

Published Papers (3 papers)

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Research

27 pages, 17769 KB  
Article
SFSMamba-DETR: Selective Feature Scanning with State Space Models and Dual-Scale Window Attention for Remote Sensing Object Detection
by Yuanli Cai, Junchao Zhao, Husheng Wu and Rui Ma
Remote Sens. 2026, 18(16), 2835; https://doi.org/10.3390/rs18162835 - 21 Aug 2026
Viewed by 197
Abstract
Object detection in remote sensing imagery remains challenging due to vast scale variations, complex backgrounds, and the prevalence of small, densely packed targets. Existing CNN-based detectors are limited by restricted receptive fields, while Transformer-based methods incur prohibitive computational overhead for high-resolution inputs. In [...] Read more.
Object detection in remote sensing imagery remains challenging due to vast scale variations, complex backgrounds, and the prevalence of small, densely packed targets. Existing CNN-based detectors are limited by restricted receptive fields, while Transformer-based methods incur prohibitive computational overhead for high-resolution inputs. In this paper, we propose SFSMamba-DETR, a detection framework that integrates state space models with Dual-Scale Window Attention for efficient and accurate remote sensing object detection. Specifically, we design a Selective Feature Scanning (SFS) module that uses the Mamba-based 2D Selective Scan mechanism to model long-range spatial dependencies with linear computational complexity. To capture both fine-grained local patterns and broader contextual cues simultaneously, we introduce a Dual-Scale Window Attention (DSWA) mechanism that operates at two complementary window scales with multi-kernel convolution bridging. These modules are orchestrated within a Cross-scale Feature Aggregation Module (CFAM) that performs hierarchical multi-scale fusion in a hybrid encoder. Extensive experiments on three primary benchmarks (MAR20, UCAS-AOD, and the Jilin-1 Satellite Aircraft Detection Dataset), together with supplementary results on DOTA and DIOR, demonstrate that SFSMamba-DETR achieves strong detection accuracy while maintaining competitive inference speed. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition (2nd Edition))
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32 pages, 6034 KB  
Article
Darwinian Wiring: A Connectome-Constrained Structural Plasticity Framework for Extreme Model Compression
by Lixing Tang, Shaohong Zhong, Wentao Gao, Jialang Liu, Yuhang Xie, Yaowen Hu, Wanqi Ma, Yingmei Wei and Yanming Guo
Remote Sens. 2026, 18(11), 1719; https://doi.org/10.3390/rs18111719 - 27 May 2026
Viewed by 468
Abstract
The deployment of lightweight object detectors on remote sensing edge platforms is severely constrained by the rigid trade-off between perception capacity and metabolic expenditure. To solve this fundamental challenge, we draw inspiration from the superior energy efficiency of the mammalian brain and the [...] Read more.
The deployment of lightweight object detectors on remote sensing edge platforms is severely constrained by the rigid trade-off between perception capacity and metabolic expenditure. To solve this fundamental challenge, we draw inspiration from the superior energy efficiency of the mammalian brain and the principles of connectomics to introduce CONERSLite. By emulating the dual mode synergy of biological neural systems, CONERSLite integrates a Compact Anatomical Backbone (CAB) representing the stable anatomical connectome and a Functional Connectome Router (FCR) that mimics the plasticity of the functional connectome. Our framework achieves a peak mAP of 82.35% on the DOTA-v1.0 dataset with only 28.3 M parameters and 195 G FLOPs, effectively establishing a new accuracy–efficiency Pareto frontier for remote sensing. On the HRSC2016 dataset, it reaches a state-of-the-art mAP of 98.62% while reducing the total parameter count by approximately 45% compared to high-precision optimized models like RTMDet. These results demonstrate that the application of connectomics principles provides a biologically grounded and highly efficient solution for resource-constrained remote sensing object detection. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition (2nd Edition))
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30 pages, 5569 KB  
Article
GRCD-Net: Guided Global–Local Relational Learning for Few-Shot Fine-Grained and Remote Sensing Scene Classification
by Jianfeng Liu, Yibo Du, Lifan Sun, Xiaozheng Li, Yanna Si, Xiaoli Song and Ruijuan Zheng
Remote Sens. 2026, 18(10), 1632; https://doi.org/10.3390/rs18101632 - 19 May 2026
Viewed by 562
Abstract
Remote sensing scene classification (RSSC) faces severe challenges from data scarcity and complex background clutter. To overcome these limitations, this paper draws inspiration from few-shot fine-grained image classification (FSFGIC) to filter noise and capture subtle details. However, existing methods often process global context [...] Read more.
Remote sensing scene classification (RSSC) faces severe challenges from data scarcity and complex background clutter. To overcome these limitations, this paper draws inspiration from few-shot fine-grained image classification (FSFGIC) to filter noise and capture subtle details. However, existing methods often process global context and local features separately, which limits their ability to suppress background noise in complex scenes. Consequently, the Guided Relational Cross-Attention Dual-branch Network (GRCD-Net) is proposed. Its core Guided Relational Cross-Attention (GRC) block leverages global semantics to filter local background noise prior to bidirectional feature interaction. Additionally, Iterative Global Relation (IGR) and Patch-level Dual-Metric (PDM) modules are integrated to robustly refine global relations and capture local similarities. Extensive experiments demonstrate that GRCD-Net consistently outperforms baselines by 2–4% on standard FSFGIC benchmarks. Notably, on the challenging NWPU-RESISC45 RSSC dataset, it achieves an 81.39% one-shot accuracy and exceeds current state-of-the-art methods by 7.55%, validating its efficacy for complex Earth observation. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition (2nd Edition))
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