New Deep Learning Paradigms For Multisource Remote Sensing Data Fusion And Classification

This article discusses the use of deep learning models in multisource remote sensing data fusion and classification. It highlights the challenges faced by traditional deep learning methods and introduces new learning paradigms such as self-supervised, weakly supervised, transfer, and federated learning. The article also invites submissions on advanced deep models and optimization strategies for remote sensing applications.