1 research outputs found
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network
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