Hyperspectral change detection has been shown to be a promising approach for detecting subtle targets in complex backgrounds. Reported change-detection methods are typically based on linear predictors that assume a space-invariant affine transformation between image pairs. Unfortunately, several physical mechanisms can lead to a significant space variance in the spectral change associated with background clutter. This may include shadowing and other illumination variations, as well as seasonal impacts on the spectral nature of the vegetation. If not properly addressed, this can lead to poor change-detection performance. This paper explores the space-varying nature of such changes through empirical measurements and investigates spectrally segmented linear predictors to accommodate these effects. Several specific algorithms are developed and applied to change imagery captured under controlled conditions, and the impacts on clutter suppression and change detection are quantified and compared. The results indicate that such techniques can provide markedly improved performance when the environmental conditions associated with the image pairs are substantially different.