[visionlist] Call for papers: ML for Remote Sensing Image/Signal Processing

[Pedro Latorre Carmona]

Call for papers

Special Issue "Machine Learning for Remote Sensing Image/Signal Processing"

Remote Sensing (MDPI) Journal

 Submission deadline: 31st December, 2021.


Dear Colleagues,



Machine learning techniques have been applied in remote sensing for more
than 20 years now. We are, however, experiencing an explosion of new
capabilities and application areas where machine learning in remote sensing
is playing and will continue to play a capital role. In particular, new
sensors with ever increasing capabilities and new computing hardware and
software capabilities are allowing us to tackle problems that were
considerably difficult to approach just a few years ago.



This Special Issue is aimed at presenting new machine learning techniques
and new application areas in remote sensing. We particularly welcome papers
focused on, although not limited to, one or more of the following topics:



·         Deep learning techniques for remote sensing

·         Machine learning techniques for inference and retrieval of
bio–geo–physical variables

·         Machine learning for remote sensing data classification and
regression

·         Multi-temporal and multi-sensor data fusion, assimilation, and
processing

·      Machine learning platforms for big data and highly demanding remote
sensing applications

·    Machine learning for multispectral and hyperspectral remote sensing
platforms and applications

·         Machine learning for uncertainty analysis and assessment in
remote sensing

·         Machine learning for remote sensing estimation and
characterization of highly variable and dynamic earth processes



We would like this Special Issue to become an example of the most
up-to-date machine learning approaches used to solve some of the problems
considered by the remote sensing community.



The website of the Special Issue can be found at:



https://www.mdpi.com/journal/remotesensing/special_issues/ML_RS_Processing



Prof. Dr. Pedro Latorre-Carmona

Prof. Dr. Antonio J. Plaza
Guest Editors
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