Region-Based Classification of Multisensor Optical-SAR Images
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.