Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis

Dear Colleagues,

Current and future high-resolution satellite earth observation missions will provide data coverage that has never been available before and with a largely untapped potential. High-resolution hyperspectral and LiDAR sensors are also gaining attention as they become cheaper in operation and more suitable for use on UAVs or small satellites. This extends the traditional set of multispectral optical and SAR imagery to new fields of application. However, there is still a lack of advance models for manipulation and exploitation of new earth observation big data. On the other hand, imagery analytics and interpretation, which are often still performed by human experts, require an increase in the level of automation in the process of value-added generation from data. Hence, powerful data mining algorithms are required to mine useful information. Even with so much literature devoted to this topic, there is still so much we do not know about machine learning models in the remote sensing field. This Special Issue aims to foster the application of advanced machine learning and deep learning algorithms to remote sensing problems. The scope is broad, but contributions with a sufficiently specific focus are preferred.

For this Special Issue, we welcome contributions related to:

• Understanding of advanced ML and DL architecture for Earth Observation data analysis;
• Transfer learning, cross-sensor learning;
• DL model fusion;
• Advanced ML models for high-resolution RS image segmentation and classification;
• High-resolution RS data fusion (Optical, SAR, and LiDAR) using ML models;
• High-resolution RS time-series analysis using ML and DL models.

Dr. Alireza Taravat
Dr. Naoto Yokoya
Prof. Jon Atli Benediktsson
Prof. Hongjun Su
Prof. Cristina Rubio-Escudero
Dr. Antonio Morales Esteban
Dr. José L. Amaro-Mellado
Prof. Francisco Martínez-Álvarez
Prof. Ata Jahangir Moshayedi
Prof. Biplab Banerjee
Ms. Mercedes Paoletti
Guest Editors

Manuscript Submission Information

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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-anonymized 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.