A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Sparse Kernel-Based Hyperspectral Anomaly Detection
2012
IEEE Geoscience and Remote Sensing Letters
In this letter, a novel ensemble-learning approach for anomaly detection is presented. The proposed technique aims to optimize an ensemble of kernel-based one-class classifiers, such as support vector data description (SVDD) classifiers, by estimating optimal sparse weights of the subclassifiers. In this method, the features of a given multivariate data set representing normalcy are first randomly subsampled into a large number of feature subspaces. An enclosing hypersphere that defines the
doi:10.1109/lgrs.2012.2187040
fatcat:5yg5om3pvrav3anr2fy5vqhrce