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7 May 2007 An automated method for pattern recognition using linear mixing model and vertex component analysis
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Abstract
Now a days detection of man made or natural object using hyperspectral imagery is a great interest of both civilian and military application. With compared to other method, hyperspectral image processing can detect both full pixel and subpixel object by analyzing the fine details of both target and background signatures. There are lots of algorithms to detect hyperspectral full pixel targets. There are also methods to detect subpixel target [1-2]. In this paper we have presented an automated method to detect hyperspectral targets using Linear Mixing Model (LMM) [4]. In our method we estimated the background endmember signatures Vertex Component Analysis which is a fast algorithm to unmix hyperspectral data [6] after removing target like pixels. Sensor noise is modeled as a Gaussian random vector with uncorrelated components of equal variance. This paper provides a complete and self-contained theoretical derivation of a subpixel target detector using the Generalized Likelihood Ratio Test (GLRT) approach and the LMM [4].
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N. Haq, M. S. Alam, and E. Sarigul "An automated method for pattern recognition using linear mixing model and vertex component analysis", Proc. SPIE 6566, Automatic Target Recognition XVII, 65660K (7 May 2007); https://doi.org/10.1117/12.720234
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