Remote Sensing for Object Detection and Change Detection: Algorithms and Applications
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 309
Editors
Interests: object detection; change detection; weakly supervised learning; remote sensing
Interests: object detection; remote sensing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; remote sensing; multimodality learning; VLM; edge intelligence
Interests: object detection; remote sensing; scene classification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of high-resolution remote sensing technology has enabled the massive acquisition of visible, hyperspectral, and other source remote sensing images. Object detection and multi-temporal change detection, as two fundamental and closely interrelated tasks in remote sensing analysis, are essential for urban monitoring, precision agriculture, disaster assessment, and ecological surveillance. However, challenges such as tiny objects, complex backgrounds, varying imaging conditions, and seasonal variations remain significant. Therefore, we organize a Special Issue entitled "Remote Sensing for Object Detection and Change Detection: Algorithms and Applications" in Remote Sensing.
This issue aims to provide a platform for sharing the latest advances in object detection and change detection algorithms and their applications. The research topics cover object detection, segmentation, change detection, and other land-cover interpretation tasks, and the data sources include, but are not limited to, optical, hyperspectral, and SAR data.
We welcome submissions relevant to addressing challenging and emerging scenarios, including open-world, limited annotations, tiny objects, dynamic environments, and cross-source or heterogeneous RSI analysis. Moreover, we also encourage submissions on novel object detection and change detection frameworks driven by foundation models, including large language models, vision-language models, and vision foundation models, through leveraging their semantic reasoning, cross-modal understanding, and generalized representation capabilities. Furthermore, applied research that utilizes deep learning methods in real-world operational systems, such as urban expansion monitoring, precision agriculture, disaster damage assessment, and ecological environment surveillance, is encouraged.
Dr. Guanchun Wang
Prof. Dr. Xiangrong Zhang
Prof. Dr. Qi Ming
Dr. Tianyang Zhang
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-anonymized 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
- object detection and change detection in remote sensing
- zero-shot/few-shot/weakly-supervised/semi-supervised/incremental learning for RSI interpretation
- multi-source, multi-temporal, and multi-modal remote sensing data fusion and interpretation
- tiny/fine-grained object detection and segmentation
- foundation model-driven (VFM, VLM, LLM) detection and change analysis
- lightweight/real-time object detection and change detection architectures
- remote sensing interpretation applications with advanced AI technology
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