Change detection using l₀ smoothing and superpixel techniques

We propose an unsupervised change detection method for satellite images using L₀ smoothing, superpixel techniques and k-means. First, we produce the difference image according to image types (synthetic aperture radar or optical images). Second, we use L₀ smoothing, an image editing method that can simultaneously sharpen major edges and smooth low-amplitude structures, to generate two difference images with distinct smooth levels. Third, k-means algorithm with k = 2 is applied on one smoothed difference image to cluster all pixels into changed or unchanged classes. Fourth, the Voronoi-Cells (VCells) algorithm is applied on the other difference image to obtain roughly uniform superpixels while preserving local image boundaries. Finally, we calculate the change degree for each superpixel, and the change detection map is produced by using k-means again. The novelties of this paper are that we use the L₀ smoothing to reduce noise and preserve edges, and utilize the spatial information with the help of superpixel. Experimental results on synthetic aperture radar and optical images show the effectiveness of our approach.