A technique for detecting man-made objects using a fractal image modeling approach is described. The technique is based on comparing multiscale signatures computed within sliding windows over coincident regions in "before" and "after" images. The signatures are computed by successive morphological erosions and dilations of the image intensity surface. Similarity measures that are a function of the surface area, fractal dimension, and fractal dimension estimation error over a range of scales are developed for discriminating between natural and man-made changes. The algorithm is applied to digitized aerial photography and SPOT satellite imagery with very encouraging results. Implementation considerations for massively-parallel architectures are discussed.