Advanced Machine Learning Approaches for Hyperspectral Data Analysis
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 43659
Special Issue Editors
Interests: multi/hyperspectral remote sensing; image processing and analysis; machine learning; pattern recognition; computer vision
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing; earthquake damage assessment; machine learning; meta-modeling; sensitivity analysis
Interests: crop yield prediction and phenotyping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last decade, hyperspectral data have become more widely available due to the development and implementation of new sensors on spaceborne, airborne, and unmanned aerial vehicle platforms, as well as on proximal systems. Such advancements have also been supported by the development of appropriate methodologies and computational approaches for data analysis. Although substantial progress has been made in this direction, multiple challenges are still open due to the high dimensionality nature of the data, which is further increased by growing spatial and spectral resolutions.
In this Special Issue, we welcome methodological contributions in terms of novel machine learning algorithms as well as the application of innovative techniques to relevant scenarios from hyperspectral data. We invite you to submit the most recent advancements in the following, and related, topics:
- Spectral data pre-processing
- Feature extraction and selection from high-dimensional data
- Machine learning and data mining methodologies for hyperspectral data analysis
- Deep, transfer, manifold, metric, and active learning
- Large-scale hyperspectral data analysis
- Methods for image segmentation and classification, change and target detection, multi-temporal analysis, hyperspectral unmixing
- Real-time processing
- Multi-modal data fusion between hyperspectral imagery with other data sources
- Advanced techniques for characterization of natural ecosystems, coastal systems, agricultural, or urban areas
Dr. Edoardo Pasolli
Dr. Gulsen Taskin
Dr. Zhou Zhang
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 data
- Machine learning
- Data pre-processing
- Dimensionality reduction
- Image classification
- Data fusion
- Spatial information
- Deep learning