Hyperspectral Image Classification Based on Adaptive Sparse Deep Network
This work addresses hyperspectral image classification for remote sensing applications, presenting an incremental advancement in sparse modeling techniques.
The paper tackled the problem of hyperspectral image classification by proposing an adaptive sparse deep network that optimizes sparse representation and regularization parameters, achieving significant improvement over traditional classifiers and other sparse model algorithms.
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.