SAR Image Change Detection Based on Hybrid Conditional Random Field

Abstract

In this letter, we propose a hybrid conditional random field (HCRF) model for synthetic aperture radar (SAR) image change detection. The HCRF model is constructed by incorporating the statistics of the log-ratio image derived from the two-temporal SAR images into conditional random field model. In this way, it is able to integrate the SAR images information, including the texture features of the two-temporal SAR images, the statistics, and the spatial interactions of the log-ratio image, into the change detection. Moreover, to achieve the integration of the information, the HCRF model consists of three parts, namely, the unary potential, the pairwise potential, and the data term modeled by the statistics of the log-ratio image. The unary potential is modeled by a support vector machine using the texture features extracted from the two-temporal SAR images, and the pairwise potential is constructed by the multilevel logistical model to capture the spatial interactions of the log-ratio image. Generalized Gamma distribution (GΓD) is utilized to model the statistics of the intensity data in the log-ratio image. Finally, experimental results on three sets of two-temporal SAR images validate the effectiveness of the proposed HCRF model.