Special Issue "Recent Advances in Multi- and Hyperspectral Image Analysis"
Deadline for manuscript submissions: 30 December 2020.
KP Labs, Konarskiego 18C, Gliwice 44-100, Poland
Interests: machine learning; deep learning; hyperspectral image analysis; satellite imaging; medical imaging; computer vision; image processing; data mining; super-resolution reconstruction
Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from the detailed information available in up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been designing a range of image-processing- and machine-learning-powered approaches toward efficient processing of such data. To this end, multi/hyperspectral analysis has bloomed and become an exciting research area which can enable faster adoption of this technology in practice, also when deployed in hardware-constrained and extreme execution environments, e.g., on board of imaging satellites.
The aim of this Special Issue is to gather and present recent advances in multi- and hyperspectral image analysis. The core themes of this topic cover all steps of the data processing pipeline, from its acquisition to final analysis and understanding. These themes include but are not limited to:
- Pre/post-processing of multi/hyperspectral images;
- Band selection from multi/hyperspectral images;
- Feature extraction and learning from multi/hyperspectral images;
- Data fusion of high-dimensional data;
- Spectral and spatial super-resolution;
- Spectral unmixing;
- Deep learning-powered algorithms for multi/hyperspectral data analysis;
- Classification and segmentation of multi/hyperspectral images;
- Multitemporal and multisensor analysis;
- Event detection and tracking;
- Prediction from multi/hyperspectral data;
- Deployment of machine/deep learning-powered techniques for multi/hyperspectral data analysis in hardware-constrained environments;
- Robustness of deep learning-powered techniques for multi/hyperspectral data analysis;
- Concept drift in multi/hyperspectral data analysis.
Dr. Jakub Nalepa
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 papers will be 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. Sensors 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 2000 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.
- Hyperspectral image analysis
- Multispectral image analysis
- Band selection
- Dimensionality reduction
- Feature extraction
- Spectral unmixing
- Data fusion
- Super-resolution reconstruction
- Machine learning
- Deep learning
- On-board processing
- Earth observation