SAR Image Change Detection Based on Variational Method and Markov Random Field Fuzzy Local Information C-Means Clustering Method

  Objectives  In order to improve the accuracy of SAR(synthetic aperture radar) image change detection, this paper proposes a method of SAR image change detection based on variational method and Markov random field fuzzy local information C-means clustering(MRFFLICM) method.

  Methods  Firstly, we fuse the logarithmic ratio images and logarithmic mean ratio images to construct the difference image. Secondly, variational denoising model is established to remove the noise from difference images. Finally, the spatial neighborhood information is introduced into fuzzy local information C-means clustering method by using Markov random field to improve the clustering performance.

  Results  Experiments on two real SAR datasets show that the proposed variational denoising method can avoid removing the small change region and effectively suppress speckle noise of SAR image.

  Conclusions  The MRFFLICM method can effectively improve the precision of change detection, thus enhancing the adaptability of change detection method.