Change detection of multispectral remote-sensing images using stationary wavelet transforms and integrated active contours

This article presents a change-detection approach for multispectral remote-sensing images. In our approach, we first exploit a wavelet-based, multi-resolution representation of the difference image. For each resolution scale, the multispectral difference image representation is considered as a 2-D Riemannian manifold embedded into a Riemannian manifold with a higher dimensionality. The integrated active contour (IAC) model is then applied to the multiband difference image representation to obtain a change-detection map at a given scale. In order to select a reasonable scale for each pixel, a measurement of regional homogeneity is defined by comparing the determinant of the metric with the average value of the metric’s determinant. For a single pixel, the final change-detection result is generated by selecting the change map on a reasonable scale. Experimental results obtained on multispectral remote-sensing images confirm the effectiveness of the proposed approach, although the time consumption of the approach is somewhat high. Our experiment achieved total error rates of 3.41%, 1.00%, and 1.95% for three data sets, which are comparable to other prevalent algorithms.