We describe and validate an automated methodology based on PPI to extract endmembers from images and distinct the
according endmembers. Four main steps are:1)project the raw image cube to its most spectral dimensions and non-noise
components by minimum noise fraction (MNF) technology; 2) use the set of spectrally distinct pixels produced by MNF
as skewers for PPI, generates a list of candidates from which final endmembers can be selected; 3) an automatic selection
procedure based on K-means clustering is consequently performed to determined the centriod of endmenbers. 4) linear
spectral mixing model (LSMM) is used to estimate mixing coefficient. And root mean square error (RMSE) reflects the
accuracy of decomposition. We use the methodology to investigate the unique properties of hyperspectral data and how
spectral information can be used to identify mineralogy with the Airborne Visible/infrared imaging Spectrometer
(AVIRIS) hyperspectral data from Cuprite, Nevada.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.