Scene class recognition using high resolution SAR/InSAR spectral decomposition methods

This paper presents a methodology for feature extraction from high resolution SAR image classification, using descriptors constructed from the complex SAR signal. The proposed data mining scheme aims at determining regions in the imaged scene which have similar content. Two complementary approaches are proposed, one making use of the single look complex data for feature extraction and the other based on the interferometric information available about the imaged scene. The features are derived from the estimated signal spectrum, in two stages. For the second stage, the model order is given by minimum number of components needed for classification and is estimated through the Akaike information criterion. Tests show that the proposed features allow for a robust recognition of 25 scene classes.