Unsupervised change detection on SAR images using markovian fusion

摘要

In this paper, we present a novel unsupervised change detection approach in temporal sets of synthetic aperture radar (SAR) images using Markovian fusion. This method is carried out within a Markovian framework which combines two different change detection algorithms to achieve noise removing and spatial information preserving at the same time. This approach is composed of two steps: 1) two change maps are generated by two distinctive but complementary approaches respectively; 2) final results are achieved by fusing the two change maps within a Markovian framework. In the first step, two different thresholding algorithms are selected to get two change maps aimed at speckle noise removing and spatial contexture preserving respectively; In the second step, a solution to fusion the two change maps through a Markov random field framework is proposed. The minimization of energy function is carried out through iterative conditional mode (ICM) algorithm because of its simplicity and moderate computation-consuming. Experiments results obtained on a SAR data set confirm the effectiveness of the proposed approach. It shows that the fusion approach based on MRFs model is a promising way of achieving robust unsupervised change detection.