华中科技大学学报（自然科学版）Issue(2)：81-85,5.DOI:10.13245/j.hust.160217
采用联合域字典稀疏表示的极化SAR图像分类
Combined dictionary learning based sparse representation for PolSAR image classification
摘要
Abstract
T raditional dictionary learning (DL ) algorithms only consider the global sparsity of data , yet ignore the spatial structure of data .Moreover ,its high computational complexity leads to the dif‐ficulty of dealing with large‐scale image data .Considering the information of PolSAR image in the spatial‐polarimetric domain , a novel combined DL based sparse representation (SR ) classification method (CDL‐SRC) was proposed for PolSAR image classification in this paper .First ,the spatial‐po‐larimetric manifold based fast affinity propagation (AP) clustering was employed to learn an over‐complete dictionary .Then locality‐constrained linear coding method was adopted to extract the spatial and polarimetric features of PolSAR respectively .Finally ,the PolSAR image was classified by the lin‐ear support vector machine (SVM ) .Compared with traditional methods ,experimental results demon‐strate that the proposed method can improve the classification accuracy ,w hich has the advantages of strong adaptability ,efficient convergence rate and low computational complexity .关键词
极化SAR图像分类/字典学习/稀疏表示/流形距离/近邻传播聚类/线性支持向量机Key words
PolSAR image classification/dictionary learning/sparse representation/manifold dis-tance/affinity propagation clustering/linear support vector machine分类
信息技术与安全科学引用本文复制引用
刘璐,刘帅,焦李成,靳少辉..采用联合域字典稀疏表示的极化SAR图像分类[J].华中科技大学学报（自然科学版）,2016,(2):81-85,5.基金项目
国家重点基础研究发展计划资助项目（2013CB329402）；国家自然科学基金资助项目（61271302，61272282）；高等学校学科创新引智计划资助项目（B07048）． （）
