Publications
Terrain classification using single-pol synthetic aperture radar
Except in extreme weather conditions, Synthetic aperture radar (SAR) is a remote sensing technology that can operate day or night. SAR can provide surveillance by making multiple passes over a wide area. For object-based intelligence, it is convenient to use these multiple passes to segment and classify the SAR images into objects that identify various terrains and man-made structures that we call "static-features." Our approach is unique in that we have multiple SAR passes of an area over a long period of time (on the order of weeks). From these many SAR images of the same area, we can combine SAR images from different times to create a variety of SAR products. For example, we introduce a novel SAR image product that captures how different regions decorrelate at different rates. From these many SAR products, we exact superpixels or groups of connected pixels that describe a homogenous region. Using pixels contained within a superpixel we develop a series of one-class classification algorithms using a goodness-of-fit metric that classifies terrains of interest in each SAR product for each superpixel. To combine the results from many SAR products we use P-value fusion. The result is a classification and a confidence about the different classes. To enforce spatial consistency, we represent the confidence labeling of the superpixels as a conditional random field and infer the most likely labeling by maximize the posterior probability of the random field. The result is a colorized SAR image where each color represents a different terrain class.