Feature Selection for Edge Detection in PolSAR Images

Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensing. This technology has the potential to provide high-resolution images regardless of the Sun’s illumination and almost independently of the atmospheric conditions. Images from PolSAR sensors are sensitive to the target’s dielectric properties and structures in several polarization states of the electromagnetic waves. Edge detection in polarimetric synthetic-aperture radar (PolSAR) imagery is challenging because of the low signal-to-noise ratio and the data format (complex matrices). There are several known marginal models stemming from the complex Wishart model for the full complex format. Each of these models renders a different likelihood. This work generalizes previous studies by incorporating the ratio of intensities as evidence for edge detection. We discuss solutions for the often challenging problem of parameter estimation. We propose a technique which rejects edge estimates built with thin evidence. Using this idea of discarding potentially irrelevant evidence, we propose a technique for fusing edge pieces of evidence from different channels that only incorporate those likely to contribute positively. We use this approach for both edge and change detection in single- and multilook images from three different sensors.