华中科技大学学报（自然科学版）Issue(z1)：187-191,5.DOI:10.13245/j.hust.15S1045
基于联合结构化稀疏表示的高光谱图像分类
Hyperspectral image classification based on joint structured sparse representation
摘要
Abstract
In order to achieve accurate hyperspectral image (HSI)classification,with combining sparse representation and the spectral information,a novel HSI classification algorithm was proposed based on joint structured sparse representation.This algorithm is able to exploit the spatial contextual information of the testing pixels and the structure information among dictionaries in the meanwhile.A joint sparse structured sparse representation model was built,and an effective solution method was developed by using the alternating direction method of multipliers (ADMM)method.Based on the proposed model,a HSI classification framework was designed based on joint structured sparse repre-sentation,in which the class-specific residue manner was adopted to determine the class of testing pix-els.The experimental results demonstrate that the proposed method can achieve better accurate classi-fication performance than other classical or state-of-the-arts algorithms.关键词
图像处理/高光谱图像/稀疏表示/联合稀疏表示/结构化稀疏表示Key words
image processing/hyperspectral image/sparse representation/joint sparse representa-tion/structured sparse representation分类
信息技术与安全科学引用本文复制引用
薄纯娟,张汝波,杨大伟,龚涛..基于联合结构化稀疏表示的高光谱图像分类[J].华中科技大学学报（自然科学版）,2015,(z1):187-191,5.基金项目
中央高校自主基金资助项目（DC201502010304，DC201501010401）；辽宁省教育厅科学研究项目（L2013504）． （）
