A measure for change detection in very high resolution remote sensing images based on texture analysis

This paper address the problem of change detection in very high resolution remote sensing images. To that end, we define a measure of the observed change based on the distribution of the coefficients issued from a wavelet transform, taking care to be rotation invariant. The dissimilarities are obtained through the Kullback-Liebler distance and a change features vector is defined from all the distances between the bands of the wavelet decomposition. This measurement is able to classify the nature of the change between two images. We present two applications: the first one uses a decision tree to classify several changes (homogeneous or oriented texture, abrupt or subtle change) whereas the second one detects some particular changes from a pair of images (an aerial and a satellite image). These experiments bring out the efficiency of the proposed technique to discriminate correctly the different textures and to interpret each change.