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.
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