Pattern Recognition
Computer Vision and Image Understanding
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
An MRF-Based Approach to Generation of Super-Resolution Images from Blurred Observations
Journal of Mathematical Imaging and Vision
Using Angular Dispersion of Gradient Direction for Detecting Edge Ribbons
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Discontinuity-Adaptive Smoothness Priors in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Segmentation of polarimetric synthetic aperture radar data
IEEE Transactions on Image Processing
Markov Random Field Model-Based Edge-Directed Image Interpolation
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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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.