Machine Learning in Remote Sensing Image Classification and Recognition

Dear Colleagues,

Recent years have witnessed the ever-growing availability of multi-source high-resolution remote sensing (RS) images (e.g., optical imagery and SAR) from sensors installed on satellites, aircraft, etc. These substantial quantities of RS images provide accurate, diverse, and complementary insights for a better understanding of Earth (e.g., fine-grained land cover classification and target recognition).

To extract meaningful information, machine learning based techniques, such as convolutional neural networks, attention mechanisms, and transformer systems, have achieved ground-breaking performances in natural image interpretation. However, several challenges and open issues remain to be addressed in the RS image classification and recognition field, including the need to develop novel methods of feature representations as well as efficient feature matching algorithms to handle high-resolution imagery on a massive scale.

This Special Issue is devoted to developing state-of-the-art machine learning methods for more accurate remote sensing classification and recognition tasks. Prospective authors are invited to submit their original unpublished contributions to this Special Issue.

Dr. Yuqi Han
Dr. Wenzheng Wang
Dr. Linbo Tang
Guest Editors

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