Convex cone-based endmember extraction for hyperspectral imagery

Wei Xiong; Ching Tsorng Tsai; Ching Wen Yang; Chein-I Chang

doi:10.1117/12.861621

13 August 2010 Convex cone-based endmember extraction for hyperspectral imagery

Wei Xiong, Ching Tsorng Tsai, Ching Wen Yang, Chein-I Chang

Proceedings Volume 7812, Imaging Spectrometry XV; 78120H (2010) https://doi.org/10.1117/12.861621
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

N-finder algorithm (N-FINDR) is a simplex-based fully abundance constrained technique which is operated on the original data space. This paper presents an approach, convex-cone N-FINDR (CC N-FINDR) which combines N-FINDR with convex cone data obtained from the original data so as to improve the N-FINDR in computational complexity and performance. The same convex cone approach can be also applied to simplex growing algorithm (SGA) to derive a new convex cone-based growing algorithm (CCGA) which also improves the SGA in the same manner as it does for NFINDR. With success in CC N-FINDR and CCGA a similar treatment of using convex cone can be further used to improve any endmember extraction algorithm (EEA). Experimental results are included to demonstrate advantages of the convex cone-based EEAs over EEAs without using convex cone.

Citation Download Citation

Wei Xiong, Ching Tsorng Tsai, Ching Wen Yang, and Chein-I Chang "Convex cone-based endmember extraction for hyperspectral imagery", Proc. SPIE 7812, Imaging Spectrometry XV, 78120H (13 August 2010); https://doi.org/10.1117/12.861621

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available