Hyperspectral Image Processing: Anomaly Detection and Classification
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 2024 | Viewed by 920
Special Issue Editors
Interests: machine learning; computation intelligence; evolutionary computation; image processing; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing; hyperspectral remote sensing; deep learning in remote sensing; change detection in remote sensing; remote sensing applications in urban planning; geospatial data analysis and modeling; SAR remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
In the study of earth science and remote sensing (RS), hyperspectral images (HSIs) have received increasing attention in recent years. Compared with other types of RS images, HSIs are composed of hundreds of continuous spectral bands and contain a large amount of spatial–spectral information, which can be used to distinguish targets of different materials at the pixel level. Reliable analysis results can be applied to many remote sensing scenarios, such as agricultural management, ecological observation, etc. To date, scholars have developed a large number of methods to classify pixels in HSI into different semantics. Initially, most of them were proposed based on traditional machine learning, such as decision trees, random forests, support vector machines, etc. With the development of deep learning, many deep networks have also been proposed, such as convolutional neural networks, recurrent neural networks, etc. However, as the content of HSIs becomes richer and the application requires more and more subdivided scenes, advanced technologies still need to be explored to fully mine the effective information of HSIs.
This Special Issue encourages the submission of papers on advanced machine/deep learning and image processing techniques for hyperspectral images.
- Hyperspectral anomaly detection;
- Hyperspectral image denoising;
- Hyperspectral image super-resolution;
- Hyperspectral target detection;
- Hyperspectral image fusion;
- Hyperspectral image classification.
Prof. Dr. Licheng Jiao
Prof. Dr. Xiangrong Zhang
Dr. Xu Tang
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
- machine learning
- deep learning
- remote sensing
- hyperspectral image
- signal processing
- image classification
- anomaly detection
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