A modified Pixel Purity Index method for hyperspectral images

Bajorski, Peter; Sanders, Niek J.

doi:10.1109/whispers.2010.5594948

Cited by 2 publications

(1 citation statement)

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“…Among them, the Pixel Purity Index (PPI) is one of the most successful algorithms. It has been widely used for endmembers extraction in hyperspectral images due to its publicity and availability in the Environment for Visualizing Images (ENVI) software [4][5]. Furthermore, it produces a relative measure for each vertex which distinguishes those near "corners" of the data with a highervalued index: this is important because the corners are where the endmembers are expected to be [6].…”

Section: Introductionmentioning

confidence: 99%

Parallel Optimization of Pixel Purity Index Algorithm for Hyperspectral Unmixing Based on Spark

Lü

et al. 2015

2015 Third International Conference on Advanced Cloud and Big Data

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The emergence of hyperspectral remote sensing has greatly promoted the development of the remote sensing technology. Endmember extraction is an important task in hyperspectral data processing. Pixel purity index (PPI)[1] algorithm has been widely used for endmember extraction in hyperspectral images. With the development of hyperspectral sensors, the resolution of hyperspectral images increases and the traditional hyperspectral processing algorithm is highly time consuming as its precision increases asymptotically. In order to process massive hyperspectral data efficiently, this paper proposes a distributed parallel implementation of PPI algorithm (PPI_DP) on cloud computing architecture. The realization of the proposed method using Spark framework and MapReduce model is described and evaluated. Experimental results demonstrate that the proposed method can effectively extract the endmembers of large quantity hyperspectral data.

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Section: Introductionmentioning

confidence: 99%