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 MoreMetadata
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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Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, Italy
Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, Italy
Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, Italy
Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Electronic and Telecommunications Engineering, University of Naples Federico II, Naples, Italy
Department of Biophysical and Electronic Engineering, University of Genoa, Genoa, Italy