20 May 2011 Hyperspectral anomaly detection using sparse kernel-based ensemble learning
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Abstract
In this paper, sparse kernel-based ensemble learning for hyperspectral anomaly detection is proposed. The proposed technique is aimed to optimize an ensemble of kernel-based one class classifiers, such as Support Vector Data Description (SVDD) classifiers, by estimating optimal sparse weights. In this method, hyperspectral signatures are first randomly sub-sampled into a large number of spectral feature subspaces. An enclosing hypersphere that defines the support of spectral data, corresponding to the normalcy/background data, in the Reproducing Kernel Hilbert Space (RKHS) of each respective feature subspace is then estimated using regular SVDD. The enclosing hypersphere basically represents the spectral characteristics of the background data in the respective feature subspace. The joint hypersphere is learned by optimally combining the hyperspheres from the individual RKHS, while imposing the l1 constraint on the combining weights. The joint hypersphere representing the most optimal compact support of the local hyperspectral data in the joint feature subspaces is then used to test each pixel in hyperspectral image data to determine if it belongs to the local background data or not. The outliers are considered to be targets. The performance comparison between the proposed technique and the regular SVDD is provided using the HYDICE hyperspectral images.
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Prudhvi Gurram, Timothy Han, Heesung Kwon, "Hyperspectral anomaly detection using sparse kernel-based ensemble learning", Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80481C (20 May 2011); doi: 10.1117/12.883383; https://doi.org/10.1117/12.883383
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