Change Detection for Hyperspectral Sensing in a Transformed Low-dimensional Space

Download or read book Change Detection for Hyperspectral Sensing in a Transformed Low-dimensional Space written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We present an approach to the problem of change in hyperspectral imagery that operates in a two-dimensional space. The coordinates in the space are related to Mahalanobis distances for the combined ('stacked') data and the individual hyperspectral scenes. Although it is only two-dimensional, this space is rich enough to include several well-known change detection algorithms, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the EC-uncorrelated detector based on heavy-tailed (elliptically contoured) clutter. Because this space is only two-dimensional, adaptive machine learning methods can produce new change detectors without being stymied by the curse of dimensionality. We investigate, in particular, the utility of the support vector machine for learning boundaries in this 2-D space, and compare the performance of the resulting nonlinearly adaptjve detector to change detectors that have themselves shown good performance.