  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.
