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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6.7 Dimensionality Reduction by Band Selection
Since different spectral bands provide different levels of information of interest the primary goal of DRBS is to select an appropriate band subset from the original band set to represent the original data in some sense of optimality. Therefore, the information preserved by DRBS has significant impact on data analysis because the information of un-selected bands will be completely lost after BS. So, a key success in DRBS is how to design effective criteria for the BS to meet various applications. Solving a general BS problem generally requires an exhaustive search for all possible 
combinations, with L and 
being the total number of spectral bands in ΩBS, respectively.
More specifically, assume that J(·) is a generic objective function of ΩBS for BS to be optimized. For a given number of selected bands, nBS, a BS technique is to find an optimal band subset, 
with 
, that satisfies the following optimization problem:
Depending on how the ...
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