Computer Vision and Features Enhancement of Hyperspectral Images
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 28 September 2025 | Viewed by 227
Special Issue Editors
Interests: deep learning; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; information fusion; image processing in remote sensing
Interests: image processing; hyperspectral image anomaly detection; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral imaging; computer vision; pattern recognition; applications to remote sensing, agriculture, environment, and medicine
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging has become a cornerstone in fields requiring detailed spectral–spatial analysis, such as remote sensing, agriculture, and biomedical imaging. Unlike traditional imaging, hyperspectral data provide a wealth of information across hundreds of wavelengths, enabling precise material identification and feature detection. However, its high dimensionality, noise, and computational demands challenge conventional analysis methods. Recent advancements in computer vision and feature enhancement techniques offer promising solutions, driving innovation in the processing, interpretation, and application of hyperspectral images.
This Special Issue seeks to showcase cutting-edge research leveraging computer vision to enhance hyperspectral image features and address associated challenges. We welcome original studies, reviews, and applications exploring novel algorithms, data integration, and practical implementations across various scales and domains. The goal is to advance methodologies that improve the accuracy, efficiency, and real-world utility of hyperspectral imaging.
Topics may include, but are not limited to, the following:
- Computer vision algorithms for hyperspectral data segmentation and classification;
- Feature extraction, enhancement, and dimensionality reduction techniques;
- Noise suppression and image quality improvement;
- Real-time processing and optimization of hyperspectral datasets;
- Integration with multisource data (e.g., LiDAR, thermal, RGB);
- Applications in environmental monitoring, precision agriculture, and medical diagnostics;
- Hyperspectral-image-based change detection and pattern recognition.
We invite contributions that bridge theory and practice, offering new insights into hyperspectral imaging through the lens of computer vision.
Prof. Dr. Hongmin Gao
Dr. Shufang Xu
Prof. Dr. Bing Tu
Prof. Dr. Jun Zhou
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 imaging
- computer vision
- feature enhancement
- image processing
- spectral analysis
- data integration
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