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Region-Based Classification of Multisensor Optical-SAR Images | IEEE Conference Publication | IEEE Xplore

Region-Based Classification of Multisensor Optical-SAR Images


Abstract:

Multispectral and synthetic aperture radar (SAR) images are known to exhibit complementary properties: unlike optical sensors, SAR provides information about the soil rou...Show More

Abstract:

Multispectral and synthetic aperture radar (SAR) images are known to exhibit complementary properties: unlike optical sensors, SAR provides information about the soil roughness and moisture, and acquires useful data despite clouds and Sun-illumination conditions. However, the analysis of the resulting images turns out to be more difficult, as compared to the use of optical imagery, due to the noise-like speckle phenomenon. In order to exploit this complementarity for classification purposes, a criticality relies in the definition of accurate joint optical-SAR statistical models, due to the different physical natures of these two data typologies and to the corresponding differences in the related parametric models. In this paper, a region-based semiparametric classification technique is proposed for multisensor optical-SAR images. The method combines the tree-structured Markov random field approach to segmentation with the dependence tree approach to probability density estimation and with case-specific bivariate models for the distributions of optical and SAR data. A Bayesian decision rule is formulated at the segment level in order to incorporate spatial-contextual information and to gain robustness against noise.
Date of Conference: 07-11 July 2008
Date Added to IEEE Xplore: 10 February 2009
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Conference Location: Boston, MA, USA

References

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