Advanced Markov random field model based on local uncertainty for unsupervised change detection

Abstract

Markov random field (MRF)-based methods are effective and popular unsupervised methods for detecting changes in remotely sensed images. In this method, the spatial contextual information is well utilized to conquer the problem of noise sensitivity in the pixel-wise change detection methods. Meanwhile, MRF also suffers from the over-smooth problem and the hard balance between denoising and detail preserving. To tackle these limitations, this letter presented an advanced MRF model based on local uncertainty (LUMRF). First, fuzzy c-means (FCM) cluster method is applied to the difference image obtained by change vector analysis to character each pixel with an initial label (change or no-change) and the corresponding membership values. To improve the detail preservation ability of MRF, the local uncertainty in a given window is subsequently computed and then integrated in the spatial energy term of MRF model. Finally, a refined change map is produced by the proposed LUMRF method. Two experiments were conducted to evaluate the effectiveness of the proposed method. The results show that, in comparison to FCM and MRF, LUMRF gives a better performance with the lowest total error detection and the performance is more robust to the parameter changes.