Machine Learning for Feature Extraction and Classification in Remote Sensing Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 16 December 2026 | Viewed by 865
Special Issue Editors
Interests: remote sensing image classification; urbanization dynamic detection; image processing
Interests: ecosystem management; geography
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are in the era of “Big Data” for Earth observation. From high-resolution optical satellites to synthetic aperture radar (SAR) and from hyperspectral imaging to nighttime light data, massive, multi-source, multi-temporal, and high-dimensional remote sensing data are accumulating at an unprecedented rate. These data provide extraordinary opportunities for advancing Earth system science. However, how to automatically, rapidly, and accurately extract physically meaningful and discriminative features from this vast “ocean of data”—and thereby achieve precise classification and identification of ground objects—remains a major bottleneck in remote sensing science. Overcoming this challenge is key to driving the evolution of remote sensing applications toward real-time, intelligent, and operational systems.
This Special Issue, “Machine Learning for Feature Extraction and Classification in Remote Sensing Images,” aims to focus on this cutting-edge interdisciplinary area. In recent years, machine learning—especially deep learning—has revolutionized remote sensing image analysis with its powerful end-to-end feature learning and complex pattern recognition capabilities. This Special Issue highlights innovative theories, models, and methodologies of machine learning for remote sensing image processing, with particular emphasis on breakthroughs that overcome the limitations of traditional approaches and achieve advances in accuracy, efficiency, and robustness.
We invite submissions of research papers covering, but not limited to, the following topics: novel deep neural network architectures, semi-supervised and unsupervised feature learning, few-shot learning, hyperspectral and SAR image classification, complex scene object recognition, target detection, and change detection.
Dr. Peng Dou
Prof. Dr. Chunlin Huang
Guest Editors
Dr. Weixiao Han
Guest Editor Assistant
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
- machine learning
- feature extraction
- image classification
- remote sensing imagery
- deep learning
- target detection
- hyperspectral imagery
- SAR imagery
- intelligent interpretation
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