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A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process
Keming Chen1,2; Zhixin Zhou,2; Chunlei Huo2
Source PublicationIEEE Geosci. Remote Sensing Lett
2013
Volume10Issue:2Pages:236-240
Abstract
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.
KeywordGaussian Processes Markov Processes Geophysical Image Processing Image Resolution Image Sensors Pattern Classification Probability  
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10850
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorKeming Chen
Affiliation1.Institute of Electronics, Chinese Academy of Sciences
2.Beijing Institute of Remote Sensing
3.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Keming Chen,Zhixin Zhou,,Chunlei Huo. A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process[J]. IEEE Geosci. Remote Sensing Lett,2013,10(2):236-240.
APA Keming Chen,Zhixin Zhou,,&Chunlei Huo.(2013).A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process.IEEE Geosci. Remote Sensing Lett,10(2),236-240.
MLA Keming Chen,et al."A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process".IEEE Geosci. Remote Sensing Lett 10.2(2013):236-240.
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