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Proceedings Paper

Statistical convex partitioning for endmember extraction
Author(s): Saeid Asadzadeh
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Paper Abstract

Endmember extraction is the process of selecting a collection of pure signature spectra of the materials present in a hyperspectral scene. Most of the spectral-based endmember extraction methods relay on the ability to discriminate between pixels based on their spectral characteristics and the assumption that pure pixels exist in the image. In some cases, though pure pixels are available inside image, spectral complexity of the image (e.g. low spectral contrast) makes it difficult to extract the best endmember candidates from hyperspectral imagery. This paper investigates the use of statistical convex partitioning (SCP) as a preprocessing tool for endmember extraction. The SCP method comprises three main steps: 1) partitioning input hyperspectral data set into partitions or so called convex regions using K-mean clustering algorithm; 2) finding the best candidate endmembers for each convex region; and, 3) comparing and listing of candidate endmembers extracted from each partition in order of spectral similarity. In order to demonstrate the performance of the proposed method, the sequential maximum angle convex cone (SMACC) algorithm was used to extract endmembers of each partition and the results were compared to pixel purity index (PPI). Optimum number of convex regions as well as the impact of different dimensionality reduction transforms, principal component analysis (PCA), minimum noise fraction (MNF), and independent component analysis (ICA) were also investigated. Experimental results on both simulated and real AVIRIS hyperspectral image indicate that SCP is an effective method to preprocess hyperspectral data spectrally and extract low contrast and similar endmembers effectively.

Paper Details

Date Published: 13 October 2010
PDF: 11 pages
Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 783105 (13 October 2010); doi: 10.1117/12.863837
Show Author Affiliations
Saeid Asadzadeh, Amirkabir Univ. of Technology (Iran, Islamic Republic of)

Published in SPIE Proceedings Vol. 7831:
Earth Resources and Environmental Remote Sensing/GIS Applications
Ulrich Michel; Daniel L. Civco, Editor(s)

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