<title>Segmentation, noise suppression, and compression of multispectral image sequences</title>

Abstract

Multispectral image sequences are one example of a class of image sequences that can be characterized as being spatially invariant. In this class of image sequences, all features are positionally invariant in each image of a given sequence but have varying gray-scale properties. The various features of the scene contribute additively to each image of the sequence but the image formation processes associated with given features have characteristic signatures describing the manner in which they vary over the image sequence. Such sequences can be processed using the simultaneous diagonalization (SD) filter which will generate gray-scale maps of the different image formation processes. The SD filter is based on an explicit mathematical model and can be used to maximize SNR, perform segmentation and provide data compression. A unique property of this approach is that even if several image formation processes occupy a given pixel, they can still be isolated. The gray-scale map associated with each process provides an estimate of the magnitude of a given process at every spatial location in the image sequence. Data compression and noise reduction can be achieved using the same spatially-invariant linearly-additive model and a variation of the simultaneous diagonalization filter.