FEATURE INTEGRATION-BASED TRAINING FOR CROSS-DOMAIN HYPERSPECTRAL IMAGE CLASSIFICATION

One difficulty in hyperspectral image (HSI) classification is that there are limited labeled examples to train a classifier. In practice, we often encounter an HSI with limited labels, while another HSI contains enough labels. Domain adaptation (DA) tries to use labeled auxiliary classes data in the second domain (i.e. source domain) to help classify classes in the first domain (i.e. target domain). The categories of source and target domains are not necessarily the same. However, the existing methods do not fully consider the conflict of data distribution between the two domains. To solve the challenge, this paper proposes a feature integration-based deep cross-domain few-shot learning (DCFSL-FI) method. Specifically, the information of source domain and target domain are integrated at the feature level, and the integrated data are used in the training process of FSL and DA at the same time, in an attempt to reduce the conflicts of data distribution and extract the common and discriminative information of the two domains. Experiments on three real datasets confirm the effectiveness of our method.