Adversarial Dilated Large-Kernel Attention Networks for Cross-Domain Few-Shot Hyperspectral Image Classification

Labeling hyperspectral image (HSI) data is time-consuming and laborious, resulting in limited labeled samples for training deep learning-based classifiers. To address this challenge, we propose ADLKAN, a cross-domain few-shot learning framework based on an adversarial dilated large kernel attention network. First, the ADLKAN consists of an adversarial dilated large kernel attention feature extractor, which can enhance the ability to capture sparse features. The feature extractor includes two cooperative spectral-spatial attention blocks with large kernel convolution, each of which extracts spectral and spatial features separately. These features are then fused to capture local contextual information and long-range dependencies in the spatial and spectral domains with attention networks. Second, ADLKAN also designs a multi-scale conditional domain discriminator to alleviate the weakness of few-shot learning under domain shift. The detailed multi-scale features are extracted by three sub-discriminators on different input scales. Finally, Adversarial training is then adopted to optimize the embedding feature extractor and strengthen its consistency in uncertain regions of samples from different domains. Experimental results on five public HSI datasets show that the proposed ADLKAN outperforms other state-of-the-art methods.