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

Change detection in hyperspectral imagery is the process of comparing two spectral images of the same scene acquired at different times, and finding a small set of pixels that has the largest apparent spectral change. We present an approach that operates in a two-dimensional space rather than in the original high-dimensional space of the images, which can be greater than 100 spectral channels. The coordinates in the 2-D space are related to Mahalanobis distances for the combined (“stacked”) data and the individual hyperspectral scenes. Several previously developed change detection algorithms can be represented as straight lines in this space, including the hyperbolic anomalous change detector, based on Gaussian scene clutter, and the EC-uncorrelated detector based on heavy-tailed (elliptically contoured) clutter. We show that adaptive machine learning methods can produce new change detectors with good performance that can avoid problems associated with the curse of dimensionality. W