Innovations in Hyperspectral Image Processing: Advancing Image Generation, Denoising, Fusion Techniques and Beyond
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 70
Special Issue Editors
Interests: machine learning; hyperspectral image processing; tensor and matrix decomposition
Interests: image restoration; image fusion; statistical machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: multimodal remote sensing image processing
Special Issues, Collections and Topics in MDPI journals
2. Ningbo Institute of Surveying, Mapping and Remote Sensing, Ningbo 315042, China
Interests: multimodal remote sensing image interpretation
Special Issues, Collections and Topics in MDPI journals
Interests: image fusion; statistical signal processing; deep learning; classification and tracking of remotely sensed images
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues，
Hyperspectral imaging (HSI) can capture hundreds of narrowband spectral responses, providing richer information than traditional imaging. Its applications include remote sensing, agriculture, and target identification and detection. However, hyperspectral data are usually noisy and have a low resolution. In some extreme cases, data are scarce, which limits the accuracy that can be achieved in subsequent tasks. Therefore, obtaining high-quality hyperspectral data is key to subsequent applications.
Recovering clean data from degraded observations is a classic inverse problem, relying heavily on prior knowledge. Hyperspectral data inherently contain rich spectral and image information. Over the recent two decades, numerous statistical regularization-based models for hyperspectral restoration have emerged, offering good interpretability and transferability. However, such models struggle to capture the data's rich structural and texture features. Meanwhile, deep learning has shown strong feature extraction capabilities and effectiveness in restoration tasks but often faces generalization issues. Thus, effectively integrating model-based approaches and data-driven techniques is key for hyperspectral restoration.
This Special Issue aims to identify innovative research that provides deep insights into HSI processing and to provide a community platform for related scholars to share ideas. Contributions that advance hyperspectral image processing are warmly welcomed for submission to this Special Issue. Articles may cover, but are not limited to, the following subjects:
- Statistical regularization models for hyperspectral restoration;
- Deep learning for hyperspectral image quality enhancement and interpretation, including denoising, super-resolution, anomaly detection, change detection, classification, and so on;
- Multi-scale and multi-modal fusion methods;
- Unsupervised and semi-supervised restoration approaches;
- Hybrid models combining deep learning and statistical regularization;
- Large-scale models for hyperspectral restoration;
- Hyperspectral image generation;
- Remote sensing foundation model;
- Reviews/surveys on recent techniques and applications.
Dr. Jiangjun Peng
Dr. Xiangyong Cao
Dr. Shuang Xu
Dr. Jing Yao
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
- hyperspectral image denoising
- hyperspectral image fusion
- hyperspectral image generation
- hyperspectral image restoration
- deep learning
- statistical regularization model
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
