A Deep Sparse Representation with Random Dictionary for Hyperspectral Image Classification
Abstract
Hyperspectral image (HSI) classification methods based on deep learning have demonstrated excellent performance, while these deep learning methods take a lot of time to train the parameters. In this paper, we propose a deep sparse representation (SR) network (DSRNet) without spending a lot of time training network parameters in the feature extraction stage. The contributions of this paper are three-fold. First, we introduce random dictionary into HSI classification, and solve sparse representation model under this dictionary. Second, we extend the shallow sparse representation model to the deep sparse representation model, where the SR model needs to be solved for each layer and used to extract the deep features of HSI. Finally, we investigate the classification performance of different classifiers on the deep features extracted by using DSRNet. Experimental results show that the proposed method can achieve better classification results compared with some closely related HSI classification methods and the other state-of-the-art deep learning methods.
