Segmented regression for spatio-spectral background estimation

We formulate hyperspectral target detection in terms of a local context by modeling the relationship of individual pixels with the annuli of pixels that surround them. A prediction of the center pixel in terms of the annulus pixels provides an estimate of the target-free pixel value, and this estimate can be used as a baseline against which a measurement of that pixel is compared. When the measurement is far from the baseline, that is evidence that the target-free hypothesis is incorrect — and that there is a target at that pixel. The predictor is adaptive to the image, and in this paper, we suggest making it more adaptive by segmenting the image into qualitatively different regions, and learning a new predictor for each region. We learn a new predictor for each segment, and attempt to optimize the segmentation so as to minimize the prediction error, overall. We apply this approach to some well-known hyperspectral datasets, and find that (as expected) the average prediction error is reduced and (less expected) that the “segments” that are discovered are spatially quite scattered, and (in the case with two segments) tend to group image pixels into edges and non-edges.