Special Issue "Unsupervised and Supervised Image Classification in Remote Sensing"
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 2441
Interests: pattern recognition; image analysis; data fusion and their applications in remote sensing; land-cover visual understanding; image classification and retrieval; spectral unmixing and image super-resolution
Interests: machine learning; signal processing and their applications in remote sensing; radar imaging; SAR interferometry and denoising; geophysical parameter estimation; semantic segmentation; scene classification and image retrieval
Interests: machine learning; pattern recognition; multisource fusion; semantic segmentation and their applications in remote sensing
The unprecedented availability of remote sensing data provides widespread opportunities to cover current and future societal needs. In this context, the accurate classification of remotely sensed images becomes a major issue for advancing on the understanding of anthropogenic changes and their environmental impact. Over the past years, machine learning techniques, especially deep learning-based ones, have certainly shown prominent results to classify remote sensing data. Nonetheless, the increasing visual complexity and data volume still raise important challenges in terms of supervised and unsupervised classification paradigms. In response, we present this special issue with the scope of cutting-edge supervised and unsupervised technologies for the accurate classification of remote sensing data.
Potential topics for this Special Issue include, but are not limited to the following:
- Pattern recognition, machine learning and deep learning techniques for remote sensing.
- Intelligent methods for classifying remote sensing images, from the scale of landscapes to ground validation data.
- Advanced remote sensing scene interpretation methods based on supervised, semi-supervised and unsupervised learning paradigms.
- New techniques for the accurate quantification of terrestrial biodiversity from remotely sensed data.
- Innovative classification models for any thematic application (urban, agricultural, ecological...) using multi-source or multi-temporal remote sensing data.
Dr. Ruben Fernandez-Beltran
Dr. Jian Kang
Dr. Renlong Hang
Dr. Jingen Ni
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 2500 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.
- image classification
- remote sensing
- machine learning