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Proceedings Paper

An automated method for pattern recognition using linear mixing model and vertex component analysis
Author(s): N. Haq; E. Sarigul; M. S. Alam
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Paper Abstract

Detection of man-made or natural object using hyperspectral sensor has attracted great research interest recently, because it can detect both the full pixel and subpixel objects by analyzing the fine details of the object as well as the background signatures. Several algorithms have been proposed in the literature to detect hyperspectral full pixel object and subpixel object. The objective of this paper is to develop an automated method to detect hyperspectral objects using the linear mixing model (LMM). Here the background is estimated from the endmember signatures using the principal component analysis (PCA) and the vertex component analysis (VCA), which is a fast algorithm to unmix hyperspectral data. Sensor noise is modeled as a Gaussian random vector with uncorrelated components of equal variance. A detail theoretical analysis of the proposed subpixel object detection algorithm has been provided using the generalized likelihood ratio test and the LMM approaches. For multipixel or resolved objects, the detection can exploit both the spatial and spectral properties. But the detection of subpixel objects can only be achieved by exploiting the spectral properties. Since the spectrum of a subpixel object is mixed with that of the background, the resultant pixel contains a combined spectral signature and hence requires some kind of (linear or nonlinear) separation of the constituent elements called the unmixing process. This paper focuses on the algorithms for detection of low probability objects, both with full pixel and subpixel. The endmembers have been evaluated assuming that only the data cube and the object signature are given. To estimate the background subspace, we have applied the PCA algorithm to the data cube and finally applied the VCA algorithm in order to estimate the background subspace signatures.

Paper Details

Date Published: 25 August 2006
PDF: 11 pages
Proc. SPIE 6313, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI, 63130I (25 August 2006); doi: 10.1117/12.679648
Show Author Affiliations
N. Haq, Univ. of South Alabama (United States)
E. Sarigul, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 6313:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVI
Franklin T. Luk, Editor(s)

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