Edge Detection in Radar Images Using Weibull Distribution

TL;DR

This paper introduces a novel edge detection method for radar images using Weibull distribution-based masks, which are more flexible than Gaussian masks, leading to improved detection of surface features.

Contribution

The paper proposes a new edge detection approach employing Weibull distribution masks, enhancing flexibility over traditional Gaussian-based methods for radar image analysis.

Findings

Weibull-based masks outperform Gaussian masks in edge detection accuracy.

The method effectively captures both symmetric and asymmetric features in radar images.

Good results demonstrated on various radar datasets.

Abstract

Radar images can reveal information about the shape of the surface terrain as well as its physical and biophysical properties. Radar images have long been used in geological studies to map structural features that are revealed by the shape of the landscape. Radar imagery also has applications in vegetation and crop type mapping, landscape ecology, hydrology, and volcanology. Image processing is using for detecting for objects in radar images. Edge detection; which is a method of determining the discontinuities in gray level images; is a very important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which is traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has limit to only symmetric shape. This paper will use to construct the masks, the Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.