Fast endmember extraction method using the geometry of the hyperspectral datacube

Shen-En Qian; Reza Rashidi Far

doi:10.1117/12.892424

16 September 2011 Fast endmember extraction method using the geometry of the hyperspectral datacube

Shen-En Qian, Reza Rashidi Far

Proceedings Volume 8157, Satellite Data Compression, Communications, and Processing VII; 815707 (2011) https://doi.org/10.1117/12.892424
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

Abstract

This paper proposes a new method to extract the endmembers of a hyperspectral datacube using the geometry of the datacube. The criterion used to find the endmembers in this method is the volume of the simplex. Unlike to the widely used endmember extraction method "N-FINDR", which calculates the volume of a simplex as many times as the number of the vertices of the simplex for each pixel of the datacube in searching for the replacers for the vertices, the proposed method calculates the volume only once for each pixel of the datacube by taking into account of the geometry of the hyperspectral datacube that is tackled. For each pixel, the proposed method finds the closest vertex of the simplex to that pixel. Then the closest vertex is replaced with the pixel for updating the simplex. Computational complexity of the proposed method is one order of magnitude less than the N-FINDR. As the proposed method is using the same criterion as N-FINDR we refer it to as fast N-FINDR (FN-FINDR). The performance of the proposed method was compared with N-FINDR using an AVIRIS datacube and a HYDICE datacube. The performance of the proposed method was evaluated using three different distance measures. The comparison was also made using two different dimensionality reduction methods. It is observed that the FN-FINDR with a modified Euclidean distance works as well as N-FINDR.

Citation Download Citation

Shen-En Qian and Reza Rashidi Far "Fast endmember extraction method using the geometry of the hyperspectral datacube", Proc. SPIE 8157, Satellite Data Compression, Communications, and Processing VII, 815707 (16 September 2011); https://doi.org/10.1117/12.892424

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KEYWORDS

Distance measurement

Principal component analysis

Evolutionary algorithms

Roads

Minerals

Hyperspectral imaging

Reconstruction algorithms

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