Discriminative Random Fields Based on Maximum Entropy Principle for Semisupervised SAR Image Change Detection

This paper proposes a novel semisupervised SAR images change detection algorithm using discriminative random fields based on maximum entropy principle (MEDRF). MEDRF is a discriminative model fused by two generative models, named as the bias model and the correction model, based on maximum entropy (ME) principle. In MEDRF model, we construct the bias model and the correction model on labeled samples and unlabeled samples, respectively, based on Markov random fields (MRF) to capture the multitemporal image information. Then, we deduce two constraints from the two generative models, and thus fuse the bias model and the correction model to derive MEDRF model according to ME principle subjected to the two constraints. In this way, the proposed MEDRF takes full advantages of the image information from the labeled samples and the unlabeled samples, especially including the spatial-contextual information, to provide an appropriate class boundary. In the experiment, we analyze the influence of the number of labeled samples to the performance of MEDRF model in semisupervised change detection to illustrate that MEDRF can achieve appropriate detection results even using a small number of labeled samples, and the experimental results on real SAR data demonstrate MEDRF model is able to achieve improvement in change detection over several methods proposed recently.