Construction of fast and robust N-FINDR algorithm

被引：0

作者：

Wang, Liguo ^{[1
]}

Jia, Xiuping

Zhang, Ye

机构：

[1] Harbin Engn Univ, Sch Informat & Commun Engn, Harbin 150001, Peoples R China

[2] Harbin Inst Technol, Dept Informat Engn, Harbin 150001, Peoples R China

[3] Univ New S Wales, Univ Coll, Sch Elect Engn, Australian Def Force Acad, Campbell, ACT 2600, Australia

来源：

INTELLIGENT COMPUTING IN SIGNAL PROCESSING AND PATTERN RECOGNITION | 2006年 / 345卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

N-FINDR has been a popular algorithm of endmember (EM) extraction method for its fully automation and relative efficiency. Unfortunately, innumerable volume calculation leads to a low speed of the algorithm and so becomes a limitation to its applications. Additionally, the algorithm is vulnerable to outliers that widely exist in hyperspectral data. In this paper, distance measure is adopted in place of volume one to speed up the algorithm and outliers are effectively controlled to endow the algorithm with robustness. Experiments show the improved algorithm is very fast and robust.

引用

页码：791 / 796

页数：6

共 50 条

[41] 纯净像元指数改进的N-FINDR高光谱端元提取算法
杨鹏飞
廖秀英
徐启恒
程辉
测绘通报, 2018, (02) : 89 - 93
[42] 两种基于异常权重的N-FINDR端元提取算法
王杰
李卫朋
西华师范大学学报(自然科学版), 2016, 37 (04) : 439 - 443
[43] SYNCHRONOUS, ASYNCHRONOUS AND GROUPING ASYNCHRONOUS PARALLEL IMPLEMENTATION FOR N-FINDR ALGORITHMS IN HYPERSPECTRAL REMOTE SENSING IMAGE
Luo, Wenfei
Zhang, Hao
2011 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2011, : 1755 - 1758
[44] 一种改进的N-FINDR高光谱端元提取算法
赵春晖
齐滨
王玉磊
电子与信息学报, 2012, 34 (02) : 499 - 503
[45] 基于改进N-FINDR算法的华北平原冬小麦面积提取
郝虑远
孙睿
谢东辉
唐侥
汪艳
农业工程学报, 2013, 29 (15) : 153 - 161
[46] FPGA Implementation of Endmember Extraction Algorithms from Hyperspectral Imagery: Pixel Purity Index versus N-FINDR
Gonzalez, Carlos
Mozos, Daniel
Resano, Javier
Plaza, Antonio
HIGH-PERFORMANCE COMPUTING IN REMOTE SENSING, 2011, 8183
[47] 以光谱信息熵改进的N-FINDR高光谱端元提取算法
杨可明
魏华锋
刘飞
史钢强
孙阳阳
地球信息科学学报, 2015, (08) : 979 - 985
[48] Segmentation of Watery Low Land Area using Hyperspectral Imaging Technique: A Comparative Study with PPI, N-FINDR, ATGP, and FIPPI
Bej, Gopinath
Dey, Tamal
Pal, Abhra
Sutradhar, Tapas
Akuli, Amitava
Ghosh, Alokesh
2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS, 2023, : 88 - 91
[49] A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data
Ayma Quirita, Victor Andres
Ostwald Pedro da Costa, Gilson Alexandre
Beltran, Cesar
REMOTE SENSING, 2022, 14 (09)
[50] FRAC: A fast and robust algorithm for classification
Fattahi, E.
Sadeghi, N.
Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT 2013, 2013, : 1047 - 1051

← 1 2 3 4 5 →