A Novel Fuzzy Inference System-Based Endmember Extraction in Hyperspectral Images

作     者：M.R.Vimala Devi S.Kalaivani 

作者机构：School of Electronics EngineeringVellore Institute of TechnologyVellore632051India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第37卷第8期

页      面：2459-2476页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Hyperspectral image spectral unmixing spectral matching endmember bundles fuzzy inference system 

摘      要：Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral *** spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental ***,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance ***,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral *** divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize *** endmember with the minimum error is chosen as the final endmember in each specific *** proposed method is simple and automatically considers endmember variability in hyperspectral *** efficiency of the proposed method is evaluated using two real hyperspectral *** average spectral angle and abundance angle are used to analyze the performance measures.