Optical images-based edge detection in Synthetic Aperture Radar images

Abstract

We address the issue of adapting optical images-based edge detection techniques for use in Polarimetric Synthetic Aperture Radar (PolSAR) imagery. We modify the gravitational edge detection technique (inspired by the Law of Universal Gravity) proposed by Lopez-Molina et al, using the non-standard neighbourhood configuration proposed by Fu et al, to reduce the speckle noise in polarimetric SAR imagery. We compare the modified and unmodified versions of the gravitational edge detection technique with the well-established one proposed by Canny, as well as with a recent multiscale fuzzy-based technique proposed by Lopez-Molina et Alejandro We also address the issues of aggregation of gray level images before and after edge detection and of filtering. All techniques addressed here are applied to a mosaic built using class distributions obtained from a real scene, as well as to the true PolSAR image; the mosaic results are assessed using Baddeley's Delta Metric. Our experiments show that modifying the gravitational edge detection technique with a non-standard neighbourhood configuration produces better results than the original technique, as well as the other techniques used for comparison. The experiments show that adapting edge detection methods from Computational Intelligence for use in PolSAR imagery is a new field worthy of exploration.Comment: Accepted for publication in Knowledge-Based System

A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes

“…They adopted a user-defined threshold in the pheromone update process to suppress noises in the detected image. Silva Jr. et al [9] modified the gravitational edge detection technique [10] with a nonstandard neighborhood configuration [11] to reduce the speckle noise in synthetic aperture radar images. For real-time edge detection, Khan et al [12] integrated a range sensor on field programmable gate arrays (FPGA) and successfully executed image normalization along with edge detection for real-time video processing.…”