Pattern Recognition in Hyperspectral Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 17801
Special Issue Editors
Interests: remote sensing image quality improvement; hyperspectral imaging; computer vision
Interests: remote sensing; hyperspectral image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: image processing; data fusion; machine learning for understanding remote sensing images; applications to disaster management and environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; image fusion; statistical signal processing; image enhancement; classification; detection; tracking
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The last 30 years have witnessed the development of hyperspectral remote sensing devices and technologies for earth observation and pattern recognition. Supported by the hyperspectral sensors, the wealthy spectral, spatial and temporal information provide plenty of opportunities for fine-grained land cover classification, mineral mapping, water quality assessment, precious farming, urban planning and monitoring, disaster management and prediction, and concealed target detection. However, with the ever-increasing application requirements, new methodological challenges appear to meet the development of more advanced techniques for efficient feature extraction, model learning, and pattern recognition. For example, how to efficiently extract reliable features from low-quality hyperspectral remote sensing data from space, how to make the most use of large-scale data when the research area changes from region to country and world, and how to recognize and detect patterns from different types of sensors.
This Special Issue aims to explore state of the art in pattern recognition applications on hyperspectral remote sensing.
Research contributions, as well as surveys, are welcome. Topics may cover advanced techniques to preprocessing, feature extraction, data fusion, cross-modality learning, material recognition, change detection, and so on. Articles may utilize advanced pattern recognition techniques to address, but are not limited, to the following topics:
- Hyperspectral imaging
- Hyperspectral image quality improvement
- Hyperspectral feature extraction and selection
- Data fusion and enhancement
- Spectral unmixing
- Multi-/cross-modal learning
- Hyperspectral classification/segmentation/detection/recognition.
Dr. Wei He
Dr. Danfeng Hong
Dr. Naoto Yokoya
Dr. Gemine Vivone
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
- pattern recognition
- hyperspectral remote sensing
- feature extraction
- hyperspectral imaging
- hyperspectral restoration
- cross-modal learning
- data fusion
- image enhancement
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