SEM algorithm and unsupervised statistical segmentation of satellite images

The work addresses Bayesian unsupervised satellite image segmentation, using contextual methods. It is shown, via a simulation study, that the spatial or spectral context contribution is sensitive to image parameters such as homogeneity, means, variances, and spatial or spectral correlations of the...

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Bibliographic Details
Published inIEEE transactions on geoscience and remote sensing Vol. 31; no. 3; pp. 618 - 633
Main Authors Masson, P., Pieczynski, W.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.05.1993
Institute of Electrical and Electronics Engineers
Subjects
Areal geology. Maps
Bayesian methods
Context modeling
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geologic maps, cartography
Histograms
Image segmentation
Iterative algorithms
Noise robustness
Parameter estimation
Random variables
Satellites
Soils
Stochastic resonance
Surficial geology
Bayes estimation
algorithms
Random field
Parameter estimation
spectral signature
Image processing
algorithm performance
imagery
data processing
segmentation
remote sensing
classification
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ISSN0196-2892
DOI10.1109/36.225529

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Summary:The work addresses Bayesian unsupervised satellite image segmentation, using contextual methods. It is shown, via a simulation study, that the spatial or spectral context contribution is sensitive to image parameters such as homogeneity, means, variances, and spatial or spectral correlations of the noise. From this one may choose the best context contribution according to the estimated values of the above parameters. The parameter estimation is done by SEM, a densities mixture estimator which is a stochastic variant of the EM (expectation-maximization) algorithm. Another simulation study shows good robustness of the SEM algorithm with respect to different image parameters. Thus, modification of the behavior of the contextual methods, when the SEM-based unsupervised approaches are considered, is limited, and the conclusions of the supervised simulation study stay valid. An adaptive unsupervised method using more relevant contextual features is proposed. Different SEM-based unsupervised contextual segmentation methods, applied to two real SPOT images, give consistently better results than a classical histogram-based method.< >
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ISSN:0196-2892
DOI:10.1109/36.225529