# Hyperspectral Image Classification Based on Adaptive Sparse Deep Network

Source: [Hyperspectral Image Classification Based on Adaptive Sparse Deep Network](<https://www.sota2.com/research/paper/68fa66941e209131cea110ee>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:53:21.106Z

Authors: Jingwen Yan, Zixin Xie, Jingyao Chen, Yinan Liu, Lei Liu

Published: 2019-10-21T14:31:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.09405>)

[PDF](<https://arxiv.org/pdf/1910.09405.pdf>)