Material identification and classification in hyperspectral imagery using the normal compositional model

David Walter Jacques Stein

doi:10.1117/12.488331

23 September 2003 Material identification and classification in hyperspectral imagery using the normal compositional model

David Walter Jacques Stein

Author Affiliations +

Proceedings Volume 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX; (2003) https://doi.org/10.1117/12.488331
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

The normal compositional model (NCM) simultaneously models subpixel mixing and intra-class variation in multidimensional imagery. It may be used as the foundation for the derivation of supervised and unsupervised classification and detection algorithms. Results from applying the algorithm to AVIRIS SWIR data collected over Cuprite, Nevada are described. The NCM class means are compared with library spectra using the Tetracorder algorithm. Of the eighteen classes used to model the data, eleven are associated with minerals that are known to be in the scene and are distinguishable in the SWIR, five are identified with Fe-bearing minerals that are not further classifiable using SWIR data, and the remaining two are spatially diffuse mixtures. The NCM classes distinguish (1) high and moderate temperature alunites, (2) dickite and kaolinite, and (3) high and moderate aluminum concentration muscovite. Estimated abundance maps locate many of the known mineral features. Furthermore, the NCM class means are compared with corresponding endmembers estimated using a linear mixture model (LMM). Of the eleven identifiable (NCM class mean, LMM endmember) pairs, ten are consistently identified, while the NCM estimation procedure reveals a diagnostic feature of the eleventh that is more obscure in the corresponding endmember and results in conflicting identifications.

Citation Download Citation

David Walter Jacques Stein "Material identification and classification in hyperspectral imagery using the normal compositional model", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); https://doi.org/10.1117/12.488331

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