Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization

Abstract

Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for few changed areas for traditional change detection methods. In this paper, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called Histogram Fitting Error Minimization (HFEM) is proposed for few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued difference image follows the Half-Normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on Half-Normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called Half-Normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only few changed datasets but datasets with lots of changes are used in the experiments. The Kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of few changed areas. Meanwhile, the proposed method can get similar results compared to traditional methods under normal conditions.