Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain
several materials. The process of determining the abundance of representative materials in a single pixel is called
spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman
iteration. We then use the unmixing information and Total Variation (TV) minimization to produce a higher
resolution hyperspectral image in which each pixel is driven towards a "pure" material. This method produces
images with higher visual quality and can be used to indicate the subpixel location of features.
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.