%0 Journal Article
%T A Study on Edge Segmentation of Different Types of Datasets with Multiple Algorithms
%A Faruque Hossain Mozumder
%A Md. Sahidul Islam
%A Md. Omar Faruq
%A Masum Miah
%A Md. Abdul Mannan
%J Journal of Computer and Communications
%P 125-135
%@ 2327-5227
%D 2025
%I Scientific Research Publishing
%R 10.4236/jcc.2025.131009
%X In this paper, we study edge detection or segmentation, which is recognized as a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s the reason it has been an influential figure in the image-processing era. Because of this, it has a significant influence in the age of image processing. On the other hand, edge detection is the process of dividing an image into discontinuous regions. It specifies the intensity shift connected to the image’s edge. There are several methods for detecting edges. Four edge identification methods on satellite images and satellite images affected by Gaussian noise were examined. Known edge detection technologies such as Canny, Prewitt, Scharr, and Robert operators are included in this study. Additionally, the key feature of an image for evaluating its quality is the Image Quality Assessment (IQA) measure. We primarily take into account SSIM, MSE, PSNR, and RMSE when assessing image quality. Experimental validation has been obtained for the application of the Canny and Prewitt algorithms to the satellite dataset. However, when the Gaussian Noise effect is added to the same dataset, clever edge detection performs better.
%K SSIM
%K MSE
%K PSNR
%K RMSE
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=140387