Unsupervised Change Detection Based on Weighted Change Vector Analysis and Improved Markov Random Field for High Spatial Resolution Imagery

Change detection is a research hotspot in the remote sensing field. In this letter, an unsupervised change detection method was proposed by optimizing two critical steps, i.e., the generation and analysis of difference image. First, the difference vectors of features are calculated using the simple differencing method. Some changed and unchanged pixels are generated by the majority voting on the results produced by clustering the difference vectors and then are used for the weight calculation of difference vectors. The weights are calculated by means of F-Score and considered in the weighted change vector analysis to produce a discriminative difference image. Finally, the change map is obtained by the improved Markov random field which takes the difference in the neighborhood pixel values into account. Experimental results on three data sets demonstrated that the proposed method outperformed six unsupervised change detection methods in terms of overall accuracy.