TY - GEN
T1 - Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images
AU - Ambikapathi, Arulmurugan
AU - Chan, Tsung Han
AU - Lin, Chia Hsiang
AU - Chi, Chong Yung
PY - 2013/6/28
Y1 - 2013/6/28
N2 - Accurate estimation of number of endmembers in a given hyper-spectral data plays a vital role in effective unmixing and identification of the materials present over the scene of interest. The estimation of number of endmembers, however, is quite challenging due to the inevitable combined presence of noise and outliers. Recently, we have proposed a convex geometry based algorithm, namely geometry based estimation of number of endmembers - affine hull (GENE-AH) [1] to reliably estimate the number of endmembers in the presence of only noise. In this paper, we will demonstrate that the GENE-AH algorithm can be suitably used for reliable estimation of number of endmembers even for data corrupted by both outliers and noise, without any prior knowledge about the outliers present in the data. Initially, the GENE-AH algorithm (alongside with its inherent endmember extraction algorithm: p-norm-based pure pixel identification (TRI-P) algorithm) is used to identify the set of candidate pixels (possibly including the outlier pixels) that contribute to the affine dimension of the hyperspectral data. Inspired by the fact that the affine hull of the hyperspectral data remains intact for any data set associated with the same endmembers (that may not be in the data set), using GENE-AH again on the corrupted data with the identified candidate pixels removed, will yield a reliable estimate of the true affine dimension (number of endmembers) of that given data. Computer simulations under various scenarios are shown to demonstrate the efficacy of the proposed methodology.
AB - Accurate estimation of number of endmembers in a given hyper-spectral data plays a vital role in effective unmixing and identification of the materials present over the scene of interest. The estimation of number of endmembers, however, is quite challenging due to the inevitable combined presence of noise and outliers. Recently, we have proposed a convex geometry based algorithm, namely geometry based estimation of number of endmembers - affine hull (GENE-AH) [1] to reliably estimate the number of endmembers in the presence of only noise. In this paper, we will demonstrate that the GENE-AH algorithm can be suitably used for reliable estimation of number of endmembers even for data corrupted by both outliers and noise, without any prior knowledge about the outliers present in the data. Initially, the GENE-AH algorithm (alongside with its inherent endmember extraction algorithm: p-norm-based pure pixel identification (TRI-P) algorithm) is used to identify the set of candidate pixels (possibly including the outlier pixels) that contribute to the affine dimension of the hyperspectral data. Inspired by the fact that the affine hull of the hyperspectral data remains intact for any data set associated with the same endmembers (that may not be in the data set), using GENE-AH again on the corrupted data with the identified candidate pixels removed, will yield a reliable estimate of the true affine dimension (number of endmembers) of that given data. Computer simulations under various scenarios are shown to demonstrate the efficacy of the proposed methodology.
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U2 - 10.1109/WHISPERS.2013.8080734
DO - 10.1109/WHISPERS.2013.8080734
M3 - Conference contribution
AN - SCOPUS:85038583048
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2013 5th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2013
Y2 - 26 June 2013 through 28 June 2013
ER -