Recent Advances in Multispectral and Hyperspectral Image Analysis and Classification
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 February 2025 | Viewed by 71
Special Issue Editors
Interests: hyperspectral intrinsic image decomposition; hyperspectral classification and target detection; remote sensing video
Interests: hyperspectral image fusion; hyperspectral classification; high-order tensor analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Multispectral/hyperspectral remote sensing imaging technology is able to analyze the structure and material characteristics of surface features by simultaneously obtaining spatial and spectral information, and it also has the ability to distinguish more types of features and invert an increased number of features. Currently, multispectral/hyperspectral remote sensing is widely used in many fields such as precision agriculture, geological surveys, land and resource surveys, atmospheric environment analysis, and military target detection.
Obtaining land/target distribution and related characteristic information from multi hyperspectral remote sensing images requires the use of many advanced analytical techniques, such as hyperspectral classification, object detection and recognition, cross-modal analysis, intrinsic image decomposition, multi hyperspectral image fusion, multi hyperspectral intrinsic decomposition, high-order signal processing, etc. Therefore, the main purpose of this Special Issue is to solicit and showcase recent techniques used for analyzing and classifying multispectral/hyperspectral remote sensing images, and to promote the development of spectral imaging technologies, especially in the fields of relative reflectance extraction, universal classification, robust target detection, etc.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Multispectral/hyperspectral imaging systems;
- Multispectral/hyperspectral classification and target detection method;
- Multispectral and hyperspectral fusion and super-resolution method;
- Multitemporal remote sensing change detection method;
- Remote sensing signal representation theory and intrinsic image decomposition;
- Multispectral/hyperspectral scene classification technology;
- Cross-domain (including temporal, scene, regional) analysis method.
Dr. Guoming Gao
Dr. Mercedes E. Paoletti
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
- hyperspectral classification
- intrinsic image decomposition
- scene classification
- high-order tensor analysis
- cross-domain adaptation
- hyperspectral and multispectral image fusion
- deep learning
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