Advances in Scene Understanding with Hyperspectral Remote Sensing: From Data Benchmarks to Applications
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 December 2025 | Viewed by 66
Special Issue Editors
Interests: hyperspectral image processing; object detection; semantic segmentation; deep learning
Interests: computer vision; deep learning; remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing; data mining; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; machine learning; unmanned aerial vehicle (UAV)-based imaging platform developments; precision agriculture; high-throughput plant phenotyping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging (HSI) has become a powerful modality for acquiring rich spectral and spatial information across a wide range of applications, such as precision agriculture, environmental monitoring, and urban planning. With recent advancements in sensor technology, machine learning, and large-scale data processing, the ability to perform automated scene understanding from hyperspectral imagery has seen remarkable progress. Nevertheless, challenges such as limited data accessibility, insufficient model generalizability, and deployment in real-world conditions continue to hinder its broader adoption. These gaps highlight the need for continued research to translate theoretical advancements into practical, scalable solutions.
This Special Issue aims to highlight cutting-edge research in hyperspectral scene understanding, fostering advancements that bridge the gap between theoretical innovation and practical implementation. Topics of interest include, but are not limited to, the following:
- Hyperspectral data benchmarks and dataset creation;
- Endmember finding and spectral unmixing;
- Subpixel-, pixel-, and object-level target detection;
- Pixel-wise and instance-level classification/segmentation;
- Spatial–spectral–temporal data analysis;
- Real-world applications and deployments.
Dr. Yanzi Shi
Dr. Mengmeng Zhang
Dr. Shou Feng
Dr. Zhou Zhang
Prof. Dr. Zhiyong Lv
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
- remote sensing
- hyperspectral image processing
- benchmarking datasets
- spectral unmixing
- target detection
- classification/segmentation
- hyperspectral applications
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