CROSS-DOMAIN ATTENTION NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Expensive cost of labeling leads to few-shot learning problem in hyperspectral image (HSI) classification. Cross-scene classification is a novel approach to solve this problem. In this work, we propose an end-to-end heterogeneous transfer learning algorithm namely cross-domain attention network (CDAN) to settle the cross-scene classification problem. CDAN mainly contains two modules. 1) A two-stream HybirdSN architecture is designed for extracting features from source and target scenes, aiming at projecting the features into a shared low-dimensional subspace. 2) Cross-domain attention mechanism is adopted based on the consistency of features between different scenes. A cross-domain updating rule is proposed for training the subnet. CDAN is proved to be effective according to the experiments on two different cross-scene HSI datasets.