Texture analysis of mean shift segmented low-resolution speckle-corrupted fractional SAR imagery through neural network classification

Abstract

The novel proposal of this work is the application of the nonparametric mean shift technique, for image segmentation, to low-resolution (LR) speckle-corrupted imagery, acquired with conventional low-cost fractional synthetic aperture radar (Fr-SAR) systems; with aims of analyzing the resultant textures, related to the remotely sensed (RS) scenes, via neural network (NN) classification. The LR speckle-corrupted recovery of the spatial reflectivity maps, provided by Fr-SAR systems, is due to the fractional synthesis mode and the different model-level and system-level operational scenario uncertainties, peculiar to such systems operating in harsh remote sensing scenarios. The mean shift segmentation method delineates arbitrarily shaped regions in the treated LR image by locating the modes in the density distribution space, and by grouping all pixels associated with the same mode. Then, the textures extracted from the segmented image are classified through NN computing, to posteriorly be used for analysis and interpretation.