Statistics-based endmember extraction algorithms for hyperspectral imagery

Shih-Yu Chu; Chao-Cheng Wu; Chein-I Chang

doi:10.1117/12.732606

12 September 2007 Statistics-based endmember extraction algorithms for hyperspectral imagery

Shih-Yu Chu, Chao-Cheng Wu, Chein-I Chang

Proceedings Volume 6661, Imaging Spectrometry XII; 66610F (2007) https://doi.org/10.1117/12.732606
Event: Photonic Devices + Applications, 2007, San Diego, California, United States

Abstract

Endmember extraction has received considerable interest in recent years. Many algorithms have been developed for this purpose and most of them are designed based on convexity geometry such as vertex or endpoint projection and maximization of simplex volume. This paper develops statistics-based approaches to endmember extraction in the sense that different orders of statistics are used as criteria to extract endmembers. The idea behind the proposed statistics-based endmember extraction algorithms (EEAs) is to assume that a set of endmmembers constitute the most un-correlated sample pool among all the same number of signatures with correlation measured by statistics which include variance specified by 2^nd order statistics, least squares error (LSE) also specified by 2^nd order statistics (variance), 3^rd order statistics (skewness), 4^th order statistics (kurtosis), k^th moment, entropy specified by infinite order of statistics and statistical independency measured by mutual information. Of particular interest are Independent Component Analysis-based EEAs which use statistics of various orders such as variance, skewness, kurtosis the kth moment and infinite orders including entropy and divergence. In order to substantiate proposed statistics-based EEAs, experiments using synthetic and real images are conducted in comparison with several popular and well-known EEAs such as Pixel Purity Index (PPI), N-finder algorithm (N-FINDR).

Citation Download Citation

Shih-Yu Chu, Chao-Cheng Wu, and Chein-I Chang "Statistics-based endmember extraction algorithms for hyperspectral imagery", Proc. SPIE 6661, Imaging Spectrometry XII, 66610F (12 September 2007); https://doi.org/10.1117/12.732606

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