Deep Learning-Driven Remote Sensing Images Interpretation and Multi-Source Collaborative 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: 31 December 2026 | Viewed by 190
Special Issue Editors
Interests: multimodal image fusion; multi-source collaborative detection
Interests: remote sensing image fusion and change detection
Interests: remote sensing image processing; data fusion; deep learning; image enhancement
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; hyperspectral image processing; hyperspectral change detection; hyperspectral image classification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing image interpretation and collaborative detection across multi-source data have become central research topics in Earth observation, driven by the rapid growth of high-resolution satellites, UAV platforms, hyperspectral sensors, SAR systems, LiDAR, and other geospatial data sources. At the same time, deep learning has significantly advanced the automatic understanding of complex remote sensing scenes, enabling more accurate object detection, land-cover mapping, change detection, target tracking, and spatiotemporal analysis. However, challenges remain in terms of data heterogeneity, modality gaps, limited annotations, domain shifts, model generalization, interpretability, and efficient fusion of multi-source information.
This Special Issue aims to present recent advances in deep learning-driven remote sensing images interpretation and multi-source collaborative detection, with a focus on innovative theories, methods, and applications that align closely with the scope of Remote Sensing. It seeks to provide a platform for reporting cutting-edge research on intelligent remote sensing data processing, feature learning, multimodal fusion, and practical Earth observation applications in environmental monitoring, urban analysis, agriculture, disaster assessment, and related fields.
We invite submissions on themes including, but not limited to:
- novel deep learning architectures (CNNs, transformers, graph neural networks) for remote sensing image classification, object detection, and semantic segmentation;
- multimodal and multi-source data fusion involving optical, SAR, hyperspectral, thermal infrared, and LiDAR data;
- foundation models and self-supervised learning for remote sensing;
- domain adaptation and transfer learning;
- cross-modal learning between optical, SAR, hyperspectral, and LiDAR data;
- interpretable and trustworthy AI for remote sensing; spatiotemporal collaborative analysis;
- change detection and temporal analysis using deep learning;
- 3D/4D reconstruction from multi-view satellite imagery;
- Accepted article types include original research articles, methodological innovations, comprehensive review papers, and benchmark datasets and performance evaluation studies.
Dr. Yinghui Xing
Dr. Kai Zhang
Prof. Dr. Liang-Jian Deng
Dr. Lifeng Wang
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
- remote sensing interpretation
- multi-source data fusion
- multi-source collaborative detection
- object detection
- land use/land cover classification
- change detection
- multi-sensor integration
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