AI Technology in Change Detection for High-Resolution Remote Sensing Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 23
Special Issue Editors
Interests: high spatial resolution remote sensing image classification; change detection; hyperspectral remote sensing image interpretation; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing of mining environments; intelligent interpretation of remote sensing imagery
Interests: remote sensing images; siamese network; change detection; convolutional neural network
Interests: deep learning; image processing; remote sensing; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: multi- and hyper-spectral remote sensing data processing; high-resolution image processing and scene analysis; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
High-resolution remote sensing images have become an essential data source for observing land surface dynamics, supporting applications such as urban expansion monitoring, land use/land cover mapping, ecological assessment, agricultural management, natural resource investigation and disaster response. With the rapid development of multi-temporal, multi-source and multi-modal Earth observation data, change detection is no longer limited to pixel-level difference analysis, but is increasingly moving toward object-level, semantic-level and process-oriented interpretation of complex land surface changes.
Despite significant progress, high-resolution remote sensing change detection still faces several challenges, including pseudo-changes caused by imaging conditions, geometric misregistration, cross-sensor discrepancies, limited annotated samples, weak generalization across scenes and insufficient interpretability. Recent advances in artificial intelligence offer new opportunities to address these issues. The self-supervised pretraining and foundation models have enhanced transferable feature representation for diverse Earth observation tasks. Vision and vision-language foundation models provide new paradigms for open-vocabulary recognition, object-level extraction and semantic change understanding. Meanwhile, generative models show potential for data augmentation, change simulation and weakly annotated sample generation. Emerging advanced architectures and learning strategies, including physics-informed learning and knowledge-guided modeling and quantum computing, are also promoting more techniques for robust, interpretable and trustworthy change detection.
We welcome original research and review articles addressing, but not limited to, the following topics:
- AI-driven change detection in high-resolution remote sensing images;
- Remote sensing foundation models and self-supervised pretraining;
- Vision-language models for semantic and open-vocabulary change detection;
- Generative AI for data augmentation, change simulation and sample generation;
- Multi-source and multimodal fusion using optical, SAR, hyperspectral, LiDAR and thermal data;
- Unsupervised, weakly supervised, semi-supervised and few-shot change detection;
- Physics-informed, knowledge-guided, trustworthy and explainable change detection;
- Quantum computing for change detection;
- Pixel-object-scene-time series level change analysis;
- Applications in urban monitoring, agriculture, ecological conservation, natural resources and disaster assessment.
Dr. Pengyuan Lv
Dr. Xunqiang Gong
Dr. Jue Wang
Dr. Zhenqi Liu
Prof. Dr. Yanfei Zhong
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-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
- high-resolution remote sensing
- artificial intelligence
- change detection
- foundation models
- vision-language models
- generative AI
- physics-informed
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
