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A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process

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5 Author(s)
Keming Chen ; Key Lab. of GeoSpatial Inf. Process. & Applic. Syst. Technol., Inst. of Electron., Beijing, China ; Zhixin Zhou ; Chunlei Huo ; Xian Sun
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In this letter, we propose a semisupervised context-sensitive technique for change detection in high-resolution multitemporal remote sensing images. This is achieved by analyzing the posterior probability of probabilistic Gaussian process (GP) classifier within a Markov random field (MRF) model. In particular, the method consists of two steps: 1) A semisupervised initialization exploits both labeled and unlabeled data based on a probabilistic GP classifier, and 2) an MRF regularization aims at refining the posterior probability by employing the spatial context information. In particular, both edge information and high-order potential are utilized in MRF energy function formulation. Experimental results obtained on real remote sensing multitemporal imagery data sets confirm the effectiveness of the proposed approach.

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Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 2 )