Deep Domain Adaptation with Second-Order Moment Alignment for Hyperspectral Image Classification

The development of deep learning technology provides an especially practical tool for hyperspectral image classification. However, the acquisition of labeled samples in a specific domain is usually time-consuming, which is not conducive to the training of neural network. In addition, different domains bring the phenomenon of 'same object but different spectrum' to hyperspectral images, which makes it difficult to directly learn from the available samples of other domains to serve a specific domain. To address this problem, we propose a deep domain adaptation network by aligning the second-order moment of source and target domain through cross-scene transfer learning. Specifically, we use abundant labeled samples in the source domain to train a 3DCNN with the purpose of identifying the target domain. Meanwhile, to reduce the distribution difference, we minimize the covariance distance between source domain and the target domain. The experimental results on two groups of hyperspectral images have shown that the proposed method can outperform several baseline methods.