Spectral-Spatial Domain Attention Network for Hyperspectral Image Few-Shot Classification

Abstract

Recently, many deep learning-based methods have been successfully applied to hyperspectral image (HSI) classification. Nevertheless, training a satisfactory network usually needs enough labeled samples. This is unfeasible in practical applications since the labeling of samples is time-consuming and expensive. The target domain samples that need to be classified are usually limited in HSIs. To mitigate this issue, a novel spectral-spatial domain attention network (SSDA) is proposed for HSI few-shot classification, which can transfer the learned classification knowledge from source domain contained enough labeled samples to target domain. The SSDA includes a spectral-spatial module, a domain attention module, and a multiple loss module. The spectral-spatial module can learn discriminative and domain invariance spectral-spatial features. The domain attention module can further enhance useful spectral-spatial features and avoid the interference of useless features. The multiple loss module, including few-shot loss, coral loss, and mmd loss, can solve the domain adaptation issue. Extensive experimental results demonstrate that on the Salinas, the University of Pavia (UP), the Indian Pines (IP), and the Huoshaoyun datasets, the proposed SSDA obtains higher classification accuracies than state-of-the art methods in the HSI few-shot classification.