SAR Image Classification via Tree-Structured Markov Random Fields and Information-Theoretic Heterogeneity Features

Segmentation algorithms are often used in many image processing applications like compression, restoration, content extraction, and classification. In particular as for the content extraction, works carried out in the past decade have demonstrated that multi-frequency fully polarimetric SAR observations content are particularly interesting, thanks to physical properties of the backscattered signal at various frequencies and polarizations. To achieve a good classification, the main difficulty is that SAR images are often embedded in heavy speckle. Segmentation of multi/hyperspectral (optical) imagery is obtained by means of algorithms based on image models, which exploit the spatial dependencies of landcovers. Unfortunately, speckle noise hides such spatial dependencies in observed SAR data. With the aim of investigating on a content extraction algorithm capable of discriminating cover classes present in the observed SAR image, homogeneity/heterogeneity features are used here to emphasize spatial dependencies in the data. Thus, observed pixel values are mapped into features, that take "similar" values on "similar" textures. This allows for using the same procedure of the optical case. Obviously, homogeneity/heterogeneity feature and segmentation quality are fundamental for classification accuracy. Here, the problem is tackled through the joint use of information-theoretic SAR features [1] and of a segmentation algorithm based on Markov Random Fields (MRFs).