Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
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N-finder algorithm (N-FINDR) is probably one of most popular and widely used algorithms for endmember extraction in hyperspectral imagery. When it comes to practical implementation, four major obstacles need to be overcome. One is the number of endmembers which must be known a priori. A second one is the use of random initial endmembers to initialize N-FINDR, which generally results in different sets of final extracted endmembers. Consequently, the results are inconsistent and not reproducible. A third one is requirement of dimensionality reduction (DR) where different used DR techniques produce different results. Finally yet importantly, it is the very expensive computational cost caused by an exhaustive search for endmembers all together simultaneously. This paper re-designs N-FINDR in a real time processing fashion to cope with these issues. Four versions of Real Time (RT) N-FINDR are developed, RT Iterative N-FINDR (RT IN-FINDR), RT SeQuential N-FINDR (RT SQ N-FINDR), RT Circular N-FINDR, RT SuCcessive N-FINDR (RT SC N-FINDR), each of which has its own merit for implementation. Experimental results demonstrate that real time processing algorithms perform as well as their counterparts with no real-time processing.