Local Preserving Graphs Using Intra-Class Competitive Representation for Dimensionality Reduction of Hyperspectral Image  

作　　者：Zhen Ye Shihao Shi Tao Sun Lin Bai 

机构地区：[1]School of Electronics and Control Engineering,Chang’an University,Xi’an 710064,China

出　　处：《Journal of Beijing Institute of Technology》2021年第2期139-158,共20页北京理工大学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.41601344);the Fundamental Research Funds for the Central Universities(Nos.300102320107 and 201924);the National Key Research and Development Project(No.2020YFC1512000);in part by the General Projects of Key R&D Programs in Shaanxi Province(No.2020GY-060);Xi’an Science&Technology Project(Nos.2020KJRC0126 and 202018)。

摘　　要：As a key technique in hyperspectral image pre-processing,dimensionality reduction has received a lot of attention.However,most of the graph-based dimensionality reduction methods only consider a single structure in the data and ignore the interfusion of multiple structures.In this paper,we propose two methods for combining intra-class competition for locally preserved graphs by constructing a new dictionary containing neighbourhood information.These two methods explore local information into the collaborative graph through competing constraints,thus effectively improving the overcrowded distribution of intra-class coefficients in the collaborative graph and enhancing the discriminative power of the algorithm.By classifying four benchmark hyperspectral data,the proposed methods are proved to be superior to several advanced algorithms,even under small-sample-size conditions.

关 键 词：intra-class competition graph construction hyperspectral image dimensionality reduction 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]