New Improvements in Parallel Implementation of N-FINDR Algorithm

Abstract

Endmember extraction (EE) is the first step in hyperspectral data unmixing. N-FINDR is one of the most commonly used EE algorithms. Nevertheless, its computational complexity is high, particularly, for a large data set. Following a parallel version of N-FINDR, i.e., P-FINDR, further improvements are presented in this paper. First, generic endmember re-extraction operation (GERO) and multiple search paths are introduced such that multiple endmembers are extracted in parallel. Second, by making full use of the advantages of the proposed algorithms, two extended schemes, i.e., extended mapping rule and multiple-stage GERO are presented, which can reduce synchronous cost and provide steady parallel performance. In experiments, the proposed algorithms have been quantitatively evaluated. The results demonstrate that they can outperform the conventional parallel computing and do not degrade the quality of EE.Index Terms-Endmember extraction (EE), hyperspectral remote sensing, N-FINDR, parallel computing.

Multiple Algorithm Integration Based on Ant Colony Optimization for Endmember Extraction From Hyperspectral Imagery

“…Similar partitioning strategies have been adopted in previous studies [23], [43]. In addition, this strategy can be easily implemented in parallel [44], [45]. Such subblock processing is also quite suitable when images cover large areas [2].…”

Nonlocal weighted sparse unmixing based on global search and parallel optimization

“…3 LMMs can be categorized as either unsupervised-or semisupervised-based unmixing algorithms. Unsupervised-based unmixing methods require endmember extraction and abundance estimation steps, 4,5 which can be classified as geometric-, [6][7][8][9][10][11][12][13][14][15][16][17][18] statistic-, [19][20][21][22][23][24] or spatial-informationbased. [25][26][27][28][29] Geometric-based algorithms usually assume that desired pure pixels exist in an image, but they usually fail if the image mixing degree is high.…”