Innovations in Remote Sensing Image Analysis
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 2026
Special Issue Editors
2. Hebei Key Laboratory of Geospatial Digital Twin and Collaborative Optimization, China University of Geosciences (Beijing), Beijing 100083, China
Interests: remote sensing; image analysis; image segmentation; feature extraction
Interests: land cover classification; weakly supervised; weakly-supervised semantic segmentation; vectorizing geo-objects; clustering
Interests: GeoAI; urban functional zone; building use; urban livability; multimodal deep learning
Interests: computer vision; image processing; pattern recognition; 3D image reconstruction; spatio-temporal image change detection and tracking; fusion and registering from imaging sensors; superresolution from low-resolution image sensors
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Special Issue Information
Dear Colleagues,
In recent decades, remote sensing image analysis has undergone significant theoretical advancements, supported by the rapid integration of cutting-edge computer science methodologies such as artificial intelligence, machine learning, and computer vision. These developments have been further facilitated by advances in remote sensing instrumentation, both in terms of data acquisition and image processing capabilities. Innovative methods have emerged across a wide range of spatial, spectral, and temporal scales, resulting in the improved interpretation of Earth observation data and its integration into various scientific and practical applications. This includes land use and land cover mapping, environmental monitoring, urban analysis, precision agriculture, disaster response, and climate change assessment, among other domains.
This Special Issue welcomes original contributions—particularly from early-career researchers—that introduce novel theories, methodologies, and applications in remote sensing image analysis. We are especially interested in works that explore new paradigms in multi-source data fusion, intelligent feature extraction, object-based analysis, deep learning models, and time-series interpretation. The research submitted may span local to global scales and include developments in sensor technologies, data pre-processing, classification, segmentation, change detection, and multidisciplinary integration. Ultimately, the aim is to push the boundaries of what is possible in Earth observation science.
Dr. Lu Xu
Dr. Xianwei Lv
Dr. Wen Zhou
Prof. Dr. Gonzalo Pajares Martinsanz
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- remote sensing
- multimodal data
- object-based image analysis
- image segmentation and classification
- deep learning
- knowledge graph
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