Robust discriminative broad learning system for hyperspectral image classification

作     者：ZHAO Liguo HAN Zhe LUO Yong ZHAO Liauo;HAN Zhe;LUO Yona

作者机构：School of Computer and Information EngineeringLuoyang Institute of Science and TechnologyLuoyang 471023China Guizhou Cloud Big Data Industry Development Co.Ltd.Guiyang 550001China 

出 版 物：《Optoelectronics Letters》 (光电子快报（英文版）)

年 卷 期：2022年第18卷第7期

页      面：444-448页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081002[工学-信号与信息处理] 

基　　金：supported by the Science and Technology Development Plan of Henan Province in 2022(No.222102210315) 

主　　题：learning has native 

摘      要：With the advantages of simple structure and fast training speed,broad learning system(BLS)has attracted attention in hyperspectral images(HSIs).However,BLS cannot make good use of the discriminative information contained in HSI,which limits the classification performance of *** this paper,we propose a robust discriminative broad learning system(RDBLS).For the HSI classification,RDBLS introduces the total scatter matrix to construct a new loss function to participate in the training of BLS,and at the same time minimizes the feature distance within a class and maximizes the feature distance between classes,so as to improve the discriminative ability of BLS *** inherits the advantages of the BLS,and to a certain extent,it solves the problem of insufficient learning in the limited HSI *** classification results of RDBLS are verified on three HSI datasets and are superior to other comparison methods.