Machine Learning and Deep Learning Applied to 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: 28 September 2025 | Viewed by 635
Special Issue Editors
Interests: remote sensing image super-resolution; deep learning; multi-modal learning
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing image processing; image generation; deep learning
Interests: remote sensing image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing technology has significantly enhanced earth observation by providing vast amounts of high-resolution multimodal data across various sensing platforms, including satellite, airborne, and UAV systems. The rapid advancement of machine learning (ML) and deep learning (DL) techniques has further improved our ability to process and interpret these data, leading to breakthroughs in land cover classification, object detection, environmental monitoring, etc.
This Special Issue will focus on the latest developments in ML and DL methods for remote sensing image analysis. We aim to highlight novel algorithms, innovative applications, and emerging trends that address the challenges of remote sensing interpretation and remote sensing image generation. We welcome original research articles, methodological contributions, and application-driven studies that will advance this field. Submissions can include work on various remote sensing data types, such as multispectral, hyperspectral, synthetic aperture radar (SAR), LiDAR, etc. Potential topics of interest include, but are not limited to, the following:
- Supervised, unsupervised, and self-supervised learning for remote sensing images;
- Advanced deep learning architectures for scene classification, object detection, image segmentation, and change detection;
- Generative models (e.g., generative adversarial networks (GANs), diffusion models, autoregressive models) for image quality improvement and data generation;
- Multimodal learning and cross-modal learning for remote sensing applications;
- Vision–language modeling for image caption, referring segmentation, visual grounding, and visual question-answering;
- Domain adaptation and transfer learning for cross-sensor and cross-region generalization;
- Real-time processing of remote sensing images.
Dr. Sen Lei
Dr. Liqin Liu
Prof. Dr. Zhengxia Zou
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
- machine learning
- deep learning
- generative models
- multimodal learning
- multispectral
- hyperspectral
- synthetic aperture radar
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