Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

In this letter, a multifeature probabilistic ensemble conditional random field (MFPECRF) model is proposed to perform the task of change detection for high spatial resolution (HSR) remote sensing imagery. MFPECRF not only considers the spectral feature of single pixels but also the interaction betwe...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2016-12, Vol.13 (12), p.1965-1969
Main Authors: Lv, Pengyuan, Zhong, Yanfei, Zhao, Ji, Jiao, Hongzan, Zhang, Liangpei
Format: Article
Language:English
Subjects:
Accuracy
Algorithm design and analysis
Change detection
Change detection algorithms
Conditional random fields
conditional random fields (CRFs)
Feature extraction
High resolution
high spatial resolution (HSR)
Image detection
Imagery
Multispectral analysis
probabilistic ensemble
Probabilistic logic
Probabilistic methods
Radar imaging
Remote sensing
Resolution
Scale models
Spatial analysis
Spatial resolution
Spectra
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Description
Summary:In this letter, a multifeature probabilistic ensemble conditional random field (MFPECRF) model is proposed to perform the task of change detection for high spatial resolution (HSR) remote sensing imagery. MFPECRF not only considers the spectral feature of single pixels but also the interaction between neighborhood pixels and the structural property of the ground objects in HSR imagery to give a higher detection accuracy than the traditional random field methods, which only utilize spectral and label information. In the unary potential, the spectral and morphological features of the difference image are combined using a probabilistic ensemble strategy, and the pairwise potential considers the contextual information of the observed field. The parameters of MFPECRF are estimated using a piecewise strategy, and the final result is obtained by the use of the loopy belief propagation algorithm. The experimental results of two groups of HSR multispectral images confirm the potential of the proposed method in improving the detection accuracy for HSR imagery.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2016.2619163