Robust and Trustworthy AI for SAR and Multi-Modal Remote Sensing Change Detection
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 20 January 2027 | Viewed by 53
Special Issue Editors
Interests: visual computing; artificial intelligence; data science; remote sensing; earth observation
Interests: remote sensing change detection; satellite image analysis; computer vision; machine learning
Interests: artificial intelligence; data science; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; big data analytics; deep learning; cybersecurity; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing change detection is an important task for monitoring environmental change, urban expansion, natural hazards, agriculture, coastal dynamics, and infrastructure risk. Synthetic aperture radar imaging is particularly valuable because it enables all-weather and day-and-night Earth observation. However, SAR image change detection remains challenging due to speckle noise, complex scattering behaviour, limited labelled data, class imbalances, and domain shifts across sensors, geographical regions, and acquisition conditions.
Recent advances in artificial intelligence have significantly improved remote sensing change detection. However, many existing models still perform well, especially on controlled benchmark datasets, and may become less reliable when applied to real-world SAR and multi-modal data. Speckle noise, sensor differences, annotation uncertainty, and environmental variability can reduce model robustness and limit operational use. Therefore, there is a clear need for change detection methods that are not only accurate but also robust, trustworthy, interpretable, and generalisable.
This Special Issue aims to bring together recent advances in speckle-robust, trustworthy, and generalisable AI methods for SAR and multi-modal remote sensing change detection. We welcome original research and review articles on robust deep learning, SAR–optical fusion, uncertainty-aware modelling, explainable AI, self-supervised and weakly supervised learning, lightweight architectures, wavelet- and attention-based methods, benchmark datasets, and real-world monitoring applications.
The Special Issue particularly encourages studies that move beyond benchmark accuracy and demonstrate reproducibility, interpretability, robustness, and operational relevance under challenging environmental and sensor conditions.
Topics of interest include, but are not limited to, the following:
- Speckle-robust SAR image change detection under noisy, imbalanced, and limited-data conditions;
- SAR–optical and multi-modal remote sensing change detection for real-world monitoring applications;
- Deep learning, attention, transformer, Mamba, wavelet-based, and frequency-aware methods for multi-temporal change detection;
- Self-supervised, semi-supervised, weakly supervised, and unsupervised change detection with limited labelled data;
- Domain generalisation, uncertainty-aware modelling, explainable AI, and trustworthy change detection across sensors, regions, and datasets.
Dr. Muhammad Shahzad
Dr. Mohamed Ihmeida
Dr. Faisal Saeed
Prof. Dr. Wadii Boulila
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
- deep learning for remote sensing
- multi-modal remote sensing
- SAR-optical fusion
- speckle-robust deep learning
- SAR image change detection
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