Deep Learning-Based Analysis of High-Resolution Remote Sensing Images: Registration, Fusion, and Change Detection
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 16 March 2026 | Viewed by 17
Special Issue Editor
Special Issue Information
Dear Colleagues,
Multi-temporal optical remote sensing change detection analyzes images from different time periods, overcoming radiometric and geometric differences to extract effective surface change information. Its scientific significance and practical value make it irreplaceable for disaster assessment and the dynamic monitoring of urban planning and environmental evolution. The core challenge lies in achieving precise registration, efficient fusion, and distinguishing real changes from false alarms in order to extract useful surface change information.
This Special Issue aims to collect the latest breakthroughs in research on deep learning for the analysis of high-resolution remote sensing images, focusing on the three core tasks: registration, fusion, and change detection. It will promote the development of a new generation of intelligent processing algorithms that can address the challenges posed by massive, multi-source, and heterogeneous remote sensing data. The goal is to enhance the automation and intelligent interpretation capabilities of Earth observation systems and provide precise decision-making support for resource monitoring, urban planning, and sustainable development.
This Special Issue welcomes original research and reviews on topics including, but not limited to, the following:
- Image Registration: Deep learning-based feature extraction and matching, non-rigid registration, cross-modal/cross-temporal image registration, unsupervised and weakly supervised registration.
- Image Fusion: Pansharpening, multi-modal fusion (e.g., optical-SAR), spatio-temporal fusion, fusion techniques based on generative models.
- Change Detection: Deep networks based on difference feature mining, direct comparison, or decision-level fusion; generalizable models addressing seasonal and illumination variations; few-shot and weakly supervised change detection; 3D change analysis.
Dr. Shikai Jiang
Guest Editor
Manuscript Submission Information
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Keywords
- deep learning
- high-resolution remote sensing images
- image registration
- image fusion
- change detection
- multi-modal data
- computer vision for remote sensing
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