Change detection in SAR images based on matrix factorisation and a Bayes classifier

Abstract

ABSTRACTChange detection in synthetic aperture radar (SAR) images can be made as a matrix factorisation model, and it can detect the changes based on the foreground information in the image. However, these methods cannot obtain satisfactory results in the change detection of SAR images because reliable background data are often not available. In this article, we propose a matrix factorisation model based on a naïve Bayes classifier to explore the low-rank and sparse information, and then detect the changes in SAR images. The factorisation model of the low-rank and sparse matrix extracts both background and foreground information from images. From the low-rank and sparse matrices, we can get the background and foreground information recovered, respectively. Then by computing the mean and variance matrix of the unchanged and changed region information, we will obtain the statistical features. The statistical features are then used to build a naïve Bayes classifier, which is used to distinguish the change detection results, and all of them are based on the acquired data distribution. The experiments, which are based on four real data sets, indicate that the approach gets a better performance than some other state-of-the-art algorithms.