Performance Analysis of Statistical-Based Pixel Purity Index Algorithms for Endmember Extraction in Hyperspectral Imagery

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 645)

Abstract

This paper presents two endmember extraction (EE) algorithms which are based on single skewer and multiple skewers, respectively, to identify the pixel purity index (PPI) in hyperspectral images. In the existing PPI algorithm, the skewers were generated randomly which can generate dissimilar results in each of the iterations, and therefore, it may lead to the increase of false alarm probability. This issue has been resolved in these EE algorithms by generating skewers using statistical parameters of the hyperspectral dataset. This reduces the false alarm probability as well as the computational complexity of the conventional PPI algorithm. This work has been experimented using cuprite dataset. Experimental results prove the effectiveness of these EE algorithms in better identification of pure pixels.

Keywords

Endmember Hyperspectral image Pixel purity index Skewer Pure pixel 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Computing Science and EngineeringVIT University - Chennai CampusChennaiIndia

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