Abstract
This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.
Highlights
Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications
This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques
This edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms
Summary
Remote sensing is a fundamental tool for comprehending the earth and supporting human–earth communications. Three research directions are suggested: (1) techniques for generating time series image datasets, (2) extraction techniques for time series imagery, and (3) applications of time series image processing in real world natures such as land, climate, disturbance attribution, vegetation dynamics, and urbanization. In light of these and many other challenges, a Special Issue of Advanced Machine Learning for Time Series Remote Sensing Data Analysis has been dedicated to address the current status, challenges, and future research priorities for the remote sensing community
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
