Deep feature dendrite with weak mapping for small-sample hyperspectral image classification

Hyperspectral image (HSI) classification faces the challenges of large and complex data and costly training labels. Existing methods for small-sample HSI classification may not achieve good generalization because they pursue powerful feature extraction and nonlinear mapping abilities. We argue that small samples need deep feature extraction but weak nonlinear mapping to achieve generalization. Based on this, we propose a Deep Feature Dendrite (DFD) method, which consists of two parts: a deep feature extraction part that uses a convolution-tokenization-attention module to effectively extract spatial-spectral features, and a controllable mapping part that uses a residual dendrite network to perform weak mapping and enhance generalization ability. We conducted experiments on four standard datasets, and the results show that our method has higher classification accuracy than other existing methods. Significance: This paper pioneers and verifies weak mapping and generalization for HSI classification (new ideas). DFD code is available at https://github.com/liugang1234567/DFD