April 2013
Intermediate to advanced
1164 pages
39h 37m
English
Content preview from Hyperspectral Data Processing: Algorithm Design and Analysis
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8.7 Experiments
The experiments conducted in this section follow the same experiments conducted in Section 7.5 so that a comparative analysis on performance evaluation between SM-EEAs in Chapter 7 and SQ-EEAs in this chapter can be performed by comparing their respective experimental results, where the same six synthetic image-based scenarios, Cuprite image and HYDICE image are used for experiments. Four categories of SQ-EEAs are selected for performance evaluation, (1) OP-based EEA, VCA, (2) simplex-based EEA, SGA, SC N-FINDR, (3) second-order statistics least-squares error-based EEAs, ATGP-EEA, UNCLS-EEA, UFCLS-EEA, and (4) high-order statistics-based EEAs, HOS-EEAs (skewness-EEA, kurtosis-EEA), ICA-EEA. So, a total of nine SQ-EEAs, VCA, SC N-FINDR, SGA, ATGP-EEA, UNCLS-EEA, UFCLS-EEA, skewness-EEA, kurtosis-EEA, and ICA-EEA are actually studied in this section for comparative analysis. Several comments on the selection of these eight SQ-EEAs are worth noting.
1. SC N-FINDR is derived as an SQ-EEA version of N-FINDR in Chapter 7.
2. SGA can also be considered as another SQ-EEA version of N-FINDR in Chapter 5, which has two versions, 1-SGA and 2-SGA, depending on one or two endmembers randomly generated for initialization.
3. VCA can be considered as an SQ-EEA of PPI in Chapter 7.
4. As will also be shown in Chapter 11, PPI, VCA, and ATGP-EEA are actually closely related through OP. With this interpretation, VCA can be considered as an SQ-EEA version of PPI and a random version ...
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