A Deep High-Order Tensor Sparse Representation for Hyperspectral Image Classification

Chunbo Cheng, Liming Zhang, Hong Li, Wenjing Cui, Junbin Gao, Yuxiao Cun

Abstract: Deep learning-based hyperspectral image (HSI) classification methods have recently shown excellent performance. However, the success of these deep learning methods mainly relies on the deep network architecture with a huge amount of parameters trained by a large number of training samples. In this article, a deep high-order tensor sparse representation (SR) network (DHTSRNet) is proposed, which can obtain better classification results in the case of small training samples. Specifically, we propose a high-order tensor SR (HTSR) model that can handle arbitrary-order tensor-type data, and extend it to a deep HTSR model that can be used to train deep high-order tensor filters and features. Then, a deep feature extraction network (DHTSRNet) based on the deep HTSR model is constructed, which is used for feature extraction of HSI. Finally, an HSI classification method is constructed by combining DHTSRNet and the classifier based on graph-based learning (GSL), which can obtain better classification results in the case of small training samples. Experimental results show that the DHTSRNet can obtain better classification performance compared with other state-of-the-art HSI classification methods.