Optical Aerial Images Change Detection Based on a Color Local Dissimilarity Map and k-Means Clustering

Considering the unavailability of labeled data sets in remote sensing change detection, this letter presents a novel and low complexity unsupervised change detection method based on the combination of similarity and dissimilarity measures: Mutual Information (MI), Disjoint Information (DI) and Local Dissimilarity Map (LDM). MI and DI are calculated on sliding windows with a step of 1 pixel for each pair of channels of both images. The resulting scalar values, weighted by q and m coefficients, are multiplied by the values of the center pixels of the windows weighted by p to remove the textures on images. The changes are detected using respectively the grayscale LDM and color LDM. A sliding window is then used on the color LDM and each pixel is characterized by a two-parameter Weibull distribution. Binarized change maps can be obtained by using a k -means clustering on the model parameters. Experiments on optical aerial image data set show that the proposed method produces comparable, even better results, to the state-of-the-art methods in terms of Recall, Precision and F-measure.