An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model  

作　　者：QIU Hong WANG Renfang JIN Heng WANG Feng 

机构地区：[1]College of Big Data and Software Engineering,Zhejiang Wanli University,Ningbo 315200,China [2]College of Information,Shanghai Ocean University,Shanghai 200120,China

出　　处：《Optoelectronics Letters》2023年第7期443-448,共6页光电子快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (No.61906170);the Project of the Science and Technology Plan for Zhejiang Province (No.LGF21F020023);the Plan Project of Ningbo Municipal Science and Technology (Nos.2022Z233,2021Z050,2022S002 and 2023J403)。

摘　　要：This paper introduces an approximate nuclear norm based matrix regression projection(ANMRP) model,an adaptive graph embedding method,for feature extraction of hyperspectral images.The ANMRP utilizes an approximate NMR model to construct an adaptive neighborhood map between samples.The globally optimal weight matrix is obtained by optimizing the approximate NMR model using fast alternating direction method of multipliers(ADMM).The optimal projection matrix is then determined by maximizing the ratio of the local scatter matrix to the total scatter matrix,allowing for the extraction of discriminative features.Experimental results demonstrate the effectiveness of ANMRP compared to related methods.

关 键 词：APPROXIMATE EMBEDDING MATRIX 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]