An Advanced Superpixel-Based Markov Random Field Model for Unsupervised Change Detection

This letter proposed an advanced superpixel-based Markov random field (MRF) model for unsupervised change detection. First, simple linear iterative clustering was implemented to the difference image to get a superpixel map. The initial change map is then obtained using fuzzy c -means by clustering the superpixel map. Finally, the initial change map of superpixel is iteratively refined by improved superpixel-based MRF that incorporated the spatial attraction model. It considers the spatial information of adjacent superpixels to complete the miss-detection superpixels and eliminate false-alarm superpixels. The experimental results over two data sets demonstrate the effectiveness of the proposed method.