By Topic

Weighted abundance-constrained linear spectral mixture analysis

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chein-I Chang ; Dept. of Comput. Sci. & Electr. Eng., Univ. of Maryland, Baltimore, MD, USA ; Baohong Ji

Linear spectral mixture analysis (LSMA) has been used in a wide range of applications. It is generally implemented without constraints due to mathematical tractability. However, it has been shown that constrained LSMA can improve unconstrained LSMA, specifically in quantification when accurate estimates of abundance fractions are necessary. As constrained LSMA is considered, two constraints are generally imposed on abundance fractions, abundance sum-to-one constraint (ASC) and abundance nonnegativity constraint (ANC), referred to as abundance-constrained LSMA (AC-LSMA). A general and common approach to solving AC-LSMA is to estimate abundance fractions in the sense of least squares error (LSE) while satisfying the imposed constraints. Since the LSE resulting from each individual band in abundance estimation is not weighted in accordance with significance of bands, the effect caused by the LSE is then assumed to be uniform over all the bands, which is generally not necessarily true. This paper extends the commonly used AC-LSMA to three types of weighted AC-LSMA resulting from three different signal processing perspectives, parameter estimation, pattern classification, and orthogonal subspace projection. As demonstrated by experiments, the weighted AC-LSMA generally performs better than unweighted AC-LSMA which can be considered as a special case of our proposed weighted AC-LSMA with the weighting matrix chosen to be the identity matrix.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:44 ,  Issue: 2 )