Simplified Conditional Random Fields With Class Boundary Constraint for Spectral-Spatial Based Remote Sensing Image Classification

Conditional random fields (CRF) have been introduced to remote sensing image classification recently to integrate contextual information into remote sensing classification. It employs the spatial property on both pixel's spectral data and labels. However, this leads to a large number of model p...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2012-09, Vol.9 (5), p.856-860
Main Authors: Zhang, Guangyun, Jia, Xiuping
Format: Article
Language:English
Subjects:
Boundaries
Classification
Conditional random fields (CRFs)
Context modeling
contextual information
Hyperspectral imaging
Image classification
Image segmentation
Markov random field (MRF)
Mathematical models
Remote sensing
Spectra
spectral-spatial classification
Training
Trains
Vectors
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Summary:Conditional random fields (CRF) have been introduced to remote sensing image classification recently to integrate contextual information into remote sensing classification. It employs the spatial property on both pixel's spectral data and labels. However, this leads to a large number of model parameters to train. In this letter, the training efficiency is improved by modifying the conventional CRF model. At the same time, a class boundary constraint is imposed into this framework to avoid over correction. The advantages of the developed method are demonstrated in the experimental results using real remotely sensed data.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2012.2186279