Sparse model is widely used in hyperspectral image classification.However, different of sparsity and regularization parameters has great influence on the classification results.In this paper, a novel adaptive sparse deep network based on deep architecture is proposed, which can construct the optimal sparse representation and regularization parameters by deep network.Firstly, a data flow graph is designed to represent each update iteration based on Alternating Direction Method of Multipliers (ADMM) algorithm.Forward network and Back-Propagation network are deduced.All parameters are updated by gradient descent in Back-Propagation.Then we proposed an Adaptive Sparse Deep Network.Comparing with several traditional classifiers or other algorithm for sparse model, experiment results indicate that our method achieves great improvement in HSI classification.
@article{arxiv.1910.09405,
title = {Hyperspectral Image Classification Based on Adaptive Sparse Deep Network},
author = {Jingwen Yan and Zixin Xie and Jingyao Chen and Yinan Liu and Lei Liu},
journal= {arXiv preprint arXiv:1910.09405},
year = {2019}
}