Deep Neural Networks for Hyperspectral Remote Sensing Image Processing (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 24
Special Issue Editors
Interests: hyperspectral image processing; multi-source remote sensing image fusion; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: thermal infrared; hyperspectral; quantitative remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral anomaly detection; network compression; efficient distributed learning
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral/multispectral image processing; medical imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Following the success of our previous Special Issue, titled “Deep Neural Networks for Hyperspectral Remote Sensing Image Processing”, we are happy to announce our next Special Issue.
A hyperspectral image (HSI) is a three-dimensional cube containing rich spatial and spectral information with hundreds of narrow and contiguous wavebands generated by an imaging spectrometer. Each pixel in hyperspectral remote sensing images corresponds to a nearly continuous spectral curve, which can reflect substances' diagnostic spectral absorption differences and provide rich spectral information for an accurate extraction of ground object information. Thanks to its high spectral resolution, hyperspectral images have received reasonable attention and have essential applications in military and civil fields. In recent years, with the continuous improvement of the hyperspectral data acquisition capability of satellites and aerial platforms, hyperspectral image processing has also developed towards big data-driven feature information extraction. However, processing the massive data collected by these platforms using traditional image analysis methodologies is impractical and ineffective. This calls for the adoption of powerful techniques that can extract reliable and useful information, where deep neural networks have been gradually applied in HSI processing due to the strong generalization and deep extraction properties of advanced semantic features.
This Special Issue aims to explore features that truly benefit hyperspectral remote sensing interpretation tasks and provides a forum for many individuals working in deep-learning-based hyperspectral image processing to report their research findings and share their experiences with the HSI community. All contributions regarding deep neural networks for hyperspectral remote sensing image processing are welcome to this Special Issue. Topics of interest include, but are not limited to, the following:
- Deep neural networks for target detection, band selection, and classification in hyperspectral images.
- Deep learning for surface parameters retrieval from thermal infrared images.
- Deep feature extraction for multi-source remote sensing images.
- The hybrid architecture of CNN and transformer for hyperspectral applications.
- Feature fusion and learning for hyperspectral image processing.
- Lightweight design of deep models.
- Reviews/surveys regarding recent applications and techniques of hyperspectral images.
We look forward to your submissions.
Dr. Yulei Wang
Dr. Enyu Zhao
Prof. Dr. Weiying Xie
Prof. Dr. Chein-I Chang
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
- deep neural networks
- deep feature extraction
- lightweight model
- hyperspectral remote sensing images
- thermal infrared
- quantitative remote sensing
- multi-sensor and multi-platform analyses
- remote sensing applications
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