Deep Learning for Spectral-Spatial Hyperspectral Image Classification (2nd Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 29 August 2025 | Viewed by 131
Special Issue Editors
Interests: urban remote sensing; urban ecology and environmental analysis; high-resolution remote sensing processing
Special Issues, Collections and Topics in MDPI journals
Interests: platform UAVs and sensors in precision farming; growth and health indices of crops; machine learning and land use simulation models
Special Issues, Collections and Topics in MDPI journals
Interests: high-dimensional spatiotemporal data mining; hyperspectral image classification
Special Issues, Collections and Topics in MDPI journals
Interests: urban remote sensing; operational land cover mapping; spatiotemporal analysis
Special Issues, Collections and Topics in MDPI journals
Interests: high spatial and hyperspectral remote sensing image processing methods and applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral imaging has expanded our ability to gather detailed data about the Earth's surface. However, effectively utilizing this rich spectral information remains a challenge. Deep learning has emerged as a promising solution, revolutionizing hyperspectral image classification by automatically learning intricate spectral–spatial patterns. We therefore welcome contributions that present advancements in this exciting field and provide insights into more accurate and impactful applications.
This Special Issue aims to explore innovative developments in the application of deep learning techniques for spectral–spatial hyperspectral image classification. Researchers are encouraged to submit original research papers, reviews, or surveys. Submissions should adhere to high scientific standards, demonstrate the significance of their contributions, and offer clear experimental validation. We welcome submissions that address both theoretical advancements and real-world applications.
This Special Issue aims to cover a wide range of topics related to deep learning for spectral–spatial hyperspectral image classification, including, but not limited to, the following:
- The development and optimization of deep neural network architectures for hyperspectral data.
- Spectral and spatial information fusion in deep learning models.
- Dimensionality reduction methods for hyperspectral data pre-processing.
- Transfer learning and domain adaptation in hyperspectral image classification.
- Data augmentation and label noise learning.
- Benchmark datasets for hyperspectral classification.
- Explainable deep learning.
- Applications in environmental monitoring, agriculture, mineral exploration, and more.
- The integration of multi-modal data sources with hyperspectral imagery.
- Multi-task learning strategies for improved hyperspectral image classification and analysis.
- The optimization of deep learning models for computational efficiency in hyperspectral image classification.
This Special Issue is the second edition of “Deep Learning for Spectral-Spatial Hyperspectral Image Classification”: https://www.mdpi.com/journal/remotesensing/special_issues/37S843D4S5.
Dr. Jiayi Li
Prof. Dr. Maria Grazia D’Urso
Dr. Xian Guo
Dr. Jie Yang
Prof. Dr. Xin Huang
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 learning
- hyperspectral imaging
- spectral–spatial classification
- domain adaption
- attention mechanisms
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