Adaptive superpixel based Markov random field model for unsupervised change detection using remotely sensed images

This study presents an adaptive superpixel based Markov Random Field (ASP_MRF) model for unsupervised remotely sensed images change detection. Firstly, the difference image is generated by change vector analysis (CVA) and the zero parameter version of the ‘simple linear iterative clustering’ method (SLICO) is applied on the difference image to obtain the superpixel map. Then, the superpixel map is initially labeled as changed and unchanged class by Fuzzy c-means (FCM) clustering method. Thirdly, the region adjacent graph (RAG) is built on the superpixel map to model the spatial constraints between the adjacent superpixels. Specially, the spectral dissimilarity between the adjacent superpixels and the label fuzziness of the neighbored superpixels were incorporated in the RAG. Lastly, The initial labels of the superpixel map are iteratively refined with ASP_MRF to generate the final change map. The experimental results prove that ASP_MRF obtained the most accurate change map and outperformed the results by pixel level MRF and superpixel based MRF, which verifies the effectiveness of ASP_MRF.