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Hyperspectral Data Processing: Algorithm Design and Analysis
book

Hyperspectral Data Processing: Algorithm Design and Analysis

by Chein-I Chang
April 2013
Intermediate to advanced
1164 pages
39h 37m
English
Wiley-Interscience
Content preview from Hyperspectral Data Processing: Algorithm Design and Analysis

22

Dynamic Dimensionality Allocation

The progressive spectral dimensionality process (PSDP) in Chapter 20 and progressive band dimensionality process (PBDP) in Chapter 21 are developed to mitigate three major issues arising in both dimensionality reduction (DR) and band selection (BS). One is that the number of dimensions to be retained after DR, q, and the number of bands for BS to select, img, must be known a priori. Another is that the values of q and img must be fixed once they are determined and cannot be changed during the process. The third one is that when the values of q and img are changed, the entire process of DR or BS must be reimplemented over again and cannot take advantage of results obtained with previous smaller values of q and img. Both PSDP and PBDP address the second and third issues by introducing dimensionality prioritization (DP) and band prioritization (BP) and the second issue by bounding q and img from below by nVD and above by 2nVD with nVD being the value determined by virtual ...

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Publisher Resources

ISBN: 9781118269770Purchase book