A performance acceleration algorithm of spectral unmixing via subset selection

Jing Ke; Yi Guo; Arcot Sowmya; Tomasz Bednarz

A performance acceleration algorithm of spectral unmixing via subset selection

Jing Ke, Yi Guo, Arcot Sowmya, Tomasz Bednarz

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper

Abstract

An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The results reveal good performance speedup with only small accuracy loss.

Original language	English
Title of host publication	Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Bruges, Belgium, April 26-28, 2017
Publisher	i6doc.com
Pages	471-476
Number of pages	6
ISBN (Print)	9782875870391
Publication status	Published - 2017
Event	European Symposium on Artificial Neural Networks - Duration: 1 Jan 2017 → …

Conference

Conference	European Symposium on Artificial Neural Networks
Period	1/01/17 → …

Cite this

@inproceedings{e6c483c517dc4f57b2aba08002144c79,

title = "A performance acceleration algorithm of spectral unmixing via subset selection",

abstract = "An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The results reveal good performance speedup with only small accuracy loss.",

author = "Jing Ke and Yi Guo and Arcot Sowmya and Tomasz Bednarz",

year = "2017",

language = "English",

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booktitle = "Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Bruges, Belgium, April 26-28, 2017",

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note = "European Symposium on Artificial Neural Networks ; Conference date: 01-01-2017",

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T1 - A performance acceleration algorithm of spectral unmixing via subset selection

AU - Ke, Jing

AU - Guo, Yi

AU - Sowmya, Arcot

AU - Bednarz, Tomasz

PY - 2017

Y1 - 2017

N2 - An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The results reveal good performance speedup with only small accuracy loss.

AB - An acceleration algorithm for spectral unmixing approach is proposed based on subset selection. The method classifies the pixels in a spectral image into accurate and approximated unmixing groups based on the similarity and dissimilarity of geomorphological features in neighboring areas. Real spectral images are used for unmixing benchmark tests for accuracy and performance verification. The results reveal good performance speedup with only small accuracy loss.

UR - https://hdl.handle.net/1959.7/uws:69327

M3 - Conference Paper

SN - 9782875870391

SP - 471

EP - 476

BT - Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017), Bruges, Belgium, April 26-28, 2017

PB - i6doc.com

T2 - European Symposium on Artificial Neural Networks

Y2 - 1 January 2017

ER -

A performance acceleration algorithm of spectral unmixing via subset selection

Abstract

Conference

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