基于块非负稀疏重构嵌入的高光谱数据降维  

Dimensionality reduction of hyperspectral data based on block nonnegative sparsity reconstruction embedding

作　　者：高阳[1] 王雪松[1] 程玉虎[1] 汪婵[1] 

机构地区：[1]中国矿业大学信息与电气工程学院,江苏徐州221116

出　　处：《控制与决策》2013年第8期1219-1225,共7页Control and Decision

基　　金：国家自然科学基金项目(61072094;61273143);教育部新世纪优秀人才支持计划项目(NCET-10-0765);教育部博士点基金项目(20110095110016;20120095110025)

摘　　要：为了在充分利用高光谱信息的同时减少因数据冗余带来的分类精度降低,提出一种块非负稀疏重构嵌入降维算法.首先,将传统超完备字典转化成超完备块字典;然后,通过计算每个超完备块字典对应样本的最小重构误差,得到块非负稀疏重构权重矩阵;最后,在低维嵌入时,通过同时最小化局部和最大化非局部高光谱数据的非负稀疏信息,得到全局最优的低维子空间高光谱数据.通过3组高光谱数据的实验结果验证了所提出方法的可行性和有效性.In order to take full advantage of high spectral information and to reduce the decline of classification accuracy resulted from data redundancy, a dimensionality reduction algorithm called block non-negative sparsity reconstruction embedding is proposed. Firstly, an ordinary over-complete dictionary is converted into an over-complete block dictionary. Then, a block non-negative sparsity reconstruction weight matrix is obtained through computing the minimum reconstruction error of the sample corresponding to each over-complete block dictionary. Finally, in the phase of low-dimensional embedding, the global optimum hyperspectral data in a low-dimensional subspace can be obtained by minimizing the local and maximizing the non-local non-negative sparse information of the hyperspectral data simultaneously. Experimental results of three groups of hyperspectral data validate the feasibility and effectiveness of the proposed algorithm.

关 键 词：高光谱数据 降维 块非负稀疏表示 全局最优 

分 类 号：TP75[自动化与计算机技术—检测技术与自动化装置]