Execution Examination of Distinctive Edge Detection Algorithms

Abstract

Edge detection or segmentation is a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s why it carries an influential Figure in the image processing era. However, the approach of partitioning an image into discontinuous parts is called edge detection. It defines the change of intensity associated with the image boundary. Edge detection can be done using a variety of approaches. This research proposed an innovative method to measure performance of four edge detection techniques using quality assessment metrics on satellite images and Gaussian noise-influenced satellite images. This paper comprises well-known edge detection technologies like Canny, Prewitt, Scharr, and Robert operators. Furthermore, the Image Quality Assessment (IQA) metric is an image’s essential characteristic for measuring image quality. For evaluating image quality, we mainly consider SSIM, MSE, PSNR, and RMSE. The execution of the Canny and Prewitt methods on the satellite dataset has been experimentally validated. However, Canny edge detection achieves better results when the Gaussian Noise effect is applied to the same dataset.