This chapter systematically sorts out the technical evolution and application paradigm of deep learning (DL) in remote sensing image (RSI) classification. First, through the differentiated feature analysis the three types of images: visible light, hyperspectral, and multispectral, the key technologies such as CNN hierarchical feature decoupling, Transformer long-term dependency modeling and lightweight model compression are revealed to improve the classification performance; then, the innovative methods such as high-spectrum feature extraction, multispectral collaborative modeling, etc., expounded from the dimensions of spatial-spectrum joint, dynamic weight allocation, and cross-modal fusion. On the data utilization level, the reduced role of small sample learning, self-supervised pre-training, and transfer learning in labeling dependency is discussed, and the data fitting framework of “weak annotation-strong generalization” is constructed. On the level of technology paradigm the dual transformation trend from single-modal feature extraction to multi-source feature deep integration, from high labeling dependency to efficient data utilization is summarized. Finally, three development directions of lightweight optimization, decision-making interpretability enhancement, and cross-domain migration ability improvement are proposed, which provide theoretical support for the construction of the full-link technology closed-loop “feature recognition-dynamic monitoring-intelligent decision” in precision agriculture, smart city and other vertical fields.