Spectral unmixing of hyperspectral images is a process by which the constituent's members of a pixel scene
are determined and the fraction of the abundance of the elements is estimated. Several algorithms have been
developed in the past in order to obtain abundance estimation from hyperspectral data, however, most of
them are characterized by being highly computational and time consuming due to the magnitude of the data
involved. In this research we present the use of Graphic Processing Units (GPUs) as a computing platform in
order to reduce computation time related to abundance estimation for hyperspectral images. Our
implementation was developed in C using NVIDIA(R) Compute Unified Device Architecture (CUDATM). The
recently introduced CUDA platform allows developers to directly use a GPU's processing power to perform
arbitrary mathematical computations. We describe our implementation of the Image Space Reconstruction
Algorithm (ISRA) and Expectation Maximization Maximum Likelihood (EMML) algorithm for abundance
estimation and present a performance comparison against implementations using C and Matlab. Results show
that the CUDA technology produced results around 10 times better than the fastest implementation done on
previous platforms.
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.