Unsupervised Change Detection in High Resolution Satellite Imagery from Fusion of Spectral and Spatial Information

In this paper, we present an unsupervised change detection approach that combines pixel-based local Haar-like features, color information, vegetation index, and man-made structure features using fuzzy logic rules to provide multi-level change detection results. An illumination invariant descriptor for each pixel is introduced to describe a Haar-like feature in a local area. Hue is used as a color feature in our change detection method. For the purpose of enhancing the change area with man-made structures, we de-weight the level of detected change areas where there are no man-made objects. The comparison of all features is done separately and the decision results are then combined under seven fuzzy logic rules to provide multi-level change detection results. The performance of the change detection is evaluated qualitatively by visual inspection and quantitatively using ground truth. The quantitative test results show that more than 90% of changes are correctly detected.