April 2013
Intermediate to advanced
1164 pages
39h 37m
English
Content preview from Hyperspectral Data Processing: Algorithm Design and AnalysisBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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14.1 Introduction
LSMA has shown success in solving a variety of problems, such as subpixel detection, mixed pixel classification, quantification, etc. It assumes that there are p image endmembers, 
, and any image pixel vector r is a linear mixture of these p endmembers with appropriate abundance fractions, 
, with αj corresponding to the abundance fraction of the jth endmember mj as follows:
where n is interpreted as a model or measurement error and 
is the endmember matrix formed by 
. Because of mathematical tractability, LSMA is widely implemented without imposing any constraint on the abundance fractions 
of the image endmembers 
. However, it has been shown in the literature, for example, Chang (2003a), that AC-LSMA can improve abundance-unconstrained LSMA in many aspects, such as ...
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