April 2013
Intermediate to advanced
1164 pages
39h 37m
English
Content preview from Hyperspectral Data Processing: Algorithm Design and Analysis
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10.5 Random SGA (RSGA)
What VCA is viewed as a sequential version of PPI is exactly what SGA is considered as a sequential version of N-FINDR in the sense that SGA finds one endmember at a time when it grows a series of simplexes with maximal volumes. Similarly, SGA can also be extended to its random version, called RSGA, in the same way as both RSQ N-FINDR and RSC N-FINDR are extended with SGA used in each run.
Random SGA (RSGA)
1. Initialization
a. Assume that the number of endmembers required to be generated is p.
b. Let ε be the given tolerance value of spectral similarity.
c. Set E(1) = 0 and 
.
.
2. Randomly generate two data sample vectors as two initial endmembers, 
.
.
3. Apply SGA to generate p endmembers, denoted by 
.
.
4. If 
, let 
and go to step 2. Otherwise, continue.
, let and go to step 2. Otherwise, continue.
5. Find the intersection of 
. In this case, a spectral measure such as SAM is used to measure spectral similarity. ...
. In this case, a spectral measure such as SAM is used to measure spectral similarity. ...Become 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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