A Semisupervised Context-Sensitive Change Detection Technique via Gaussian Process
Keming Chen1,2; Zhixin Zhou,2; Chunlei Huo2
2013
发表期刊IEEE Geosci. Remote Sensing Lett
卷号10期号:2页码:236-240
摘要
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.
关键词Gaussian Processes Markov Processes Geophysical Image Processing Image Resolution Image Sensors Pattern Classification Probability  
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10850
专题模式识别国家重点实验室_先进数据分析与学习
通讯作者Keming Chen
作者单位1.Institute of Electronics, Chinese Academy of Sciences
2.Beijing Institute of Remote Sensing
3.Institute of Automation, Chinese Academy of Sciences
推荐引用方式
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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