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Advances in Video Satellite Remote Sensing and Moving Target Monitoring

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Editors

Dr. Guoming Gao

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Guest Editor

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: satellite video data processing and target Monitoring; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shengyang Li

E-Mail Website
Guest Editor

Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China
Interests: image/video intelligent processing and analysis; big data modeling and analysis; aerospace ground data system technology

Special Issue Information

Dear Colleagues,

Video satellite is a new type of Earth observation satellite that has rose in popularity in recent years. It can perform regional staring imaging and obtain continuous video image data of the region. Notably, it exhibits a relatively unchanged background while capturing a wealth of moving targets. Therefore, video satellite is especially suitable for regional dynamic change monitoring, such as situation change, dynamic target reconnaissance and surveillance, high dynamic disaster monitoring, wide area traffic monitoring, and attack effect evaluation. Given the extensive potential of satellite video applications, many researchers have explored related processing techniques, including target tracking, dynamic scene classification, motion object detection, video super-resolution, and video signal analysis. However, the characteristics of dim-small targets, complex lighting changes, and easily occluded targets pose significant challenges to video processing methods. In the future, new artificial intelligence and video signal processing methods will hopefully bring about new breakthroughs in satellite video applications.

This Special Issue aims to collate studies that cover novel staring video remote sensing satellites and their applications in high-dynamic target monitoring and dynamic scene understanding. Topics of interest include research on the design of new staring video satellites, video satellite networking and mission planning, satellite video data representation and signal enhancement, intelligent detection and tracking of moving targets, dynamic scene analysis, and traffic flow analysis. Hence, staring video satellite design, planning, intelligent data processing, and multifield applications, among other issues, are welcome to be focused on.

We encourage submissions of both regular research papers and reviews on topics including, but not limited to, the following:

Satellite video data processing and application;
Dynamic target detection and tracking;
Dynamic scene understanding;
Satellite video enhancement and dim-small target enhancement;
Satellite video intrinsic decomposition and component separation;
Use of AI methods in satellite video processing;
Video satellite design and mission planning.

Dr. Guoming Gao
Prof. Dr. Shengyang Li
Guest Editors

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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.

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27 pages, 2697 KB

Open AccessArticle

S2A-Swin: Spectral Smoothing–Guided Spectral–Spatial Windows with Generative Augmentation for Hyperspectral Image Classification Under Class Imbalance and Limited Labels

by Baisen Liu, Jianxin Chen, Wulin Zhang, Zhiming Dang, Xinyao Li and Weili Kong

Remote Sens. 2026, 18(6), 935; https://doi.org/10.3390/rs18060935 - 19 Mar 2026

Viewed by 603

Abstract

Hyperspectral image (HSI) classification faces the challenges of scarce labeled data and severe class imbalance, which limits the effective training and generalization capabilities of models. To address these issues, we propose S2A-Swin, a joint spatial–spectral hybrid Swin Transformer framework. First, we develop a spectral–spatial conditional generative adversarial network (SSC-cGAN), which combines spectral and spatial smoothing regularizers to synthesize class-specific image patches, thus alleviating the problems of data scarcity and class imbalance while maintaining spectral continuity and local spatial structure consistent with real data. Second, we introduce a dimension-aware hybrid Transformer module, which adds local windows along the spectral dimension to the standard spatial window, thereby facilitating cross-dimensional feature interactions and ensuring that each spectral band is modeled using the local spatial context for more efficient joint spatial–spectral modeling. In this module, attention mechanisms for spectral and spatial windows are applied alternately (“cross-sequence” attention mechanisms), the execution order of which is guided by hyperspectral prior knowledge to enhance cross-dimensional representation learning. This module is embedded in the lightweight Swin backbone and extends the traditional spatial window mechanism through spectral window attention, capturing spectral continuity while maintaining spatial structure consistency. Extensive experiments on multiple datasets demonstrate that, compared to mainstream CNN and Transformer baselines on four benchmark datasets, the proposed method achieves overall accuracy (OA) improvements of 2.45%, 7.05%, 5.17%, and 0.85%. Full article

(This article belongs to the Special Issue Advances in Video Satellite Remote Sensing and Moving Target Monitoring)

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