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-, using the non-standard neighbourhood configuration proposed by , 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- 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.
A more recent multi-scale edge detection method was proposed by Lopez- @cite_18 , using Sobel operators for edge extraction and the concept of Gaussian scale-space. More specifically, the Sobel edge detection method is applied on increasingly smoother versions of the image. Then, the edges which appear on different scales are combined by performing coarse-to-fine edge tracking.
@cite_18
2023578721
The human vision is usually considered a multiscale, hierarchical knowledge extraction system. Inspired by this fact, multiscale techniques for computer vision perform a sequential analysis, driven by different interpretations of the concept of scale. In the case of edge detection, the scale usually relates to the size of the region where the intensity changes are measured or to the size of the regularization filter applied before edge extraction. Multiscale edge detection methods constitute an effort to combine the spatial accuracy of fine-scale methods with the ability to deal with spurious responses inherent to coarse-scale methods. In this work we introduce a multiscale method for edge detection based on increasing Gaussian smoothing, the Sobel operators and coarse-to-fine edge tracking. We include visual examples and quantitative evaluations illustrating the benefits of our proposal.