An improved MRF-based change detection approach for multitemporal remote sensing imagery

In the task of multitemporal remote sensing image change detection, conventional Markov random field (MRF) based approaches consider contextual information between neighboring pixels to obtain the change map. However, these approaches often get erroneous results at discontinuities such as edges, ridges and valleys, since they assume that neighboring pixels tend to have the same label. To overcome this, an improved MRF based change detection approach for multitemporal remote sensing imagery is proposed. The method first finds edges in the difference image by using the line process. Then, the weights of MRF prior energy are adaptively adjusted by considering the gray level differences between neighboring pixels. A group of adaptive weighting functions are defined in the study, and their performances in the task of change detection are compared. Experimental results confirm the proposed approach.

► The edges in the difference image are first found by line process.
► Conditions of a function to be adaptive for change detection are defined.
► Adaptive weighting functions (AWFs) are defined according to gray level difference.
► The performance of our method is robust to the choice of AWFs and noise.