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.
References
[1]
Zhan, C., Duan, X., Xu, S., Song, Z. and Luo, M. (2007) An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection. Fourth International Conference on Image and Graphics (ICIG 2007), Chengdu, 22-24 August 2007, 519-523. https://doi.org/10.1109/icig.2007.153
[2]
Application of Edge Detection. https://www.slideshare.net/NareshBiloniya/application-of-edge-detection-96942725
[3]
Bhardwaj, S. and Mittal, A. (2012) A Survey on Various Edge Detector Techniques. Procedia Technology, 4, 220-226. https://doi.org/10.1016/j.protcy.2012.05.033
[4]
Jain, R., Kasturi, R. and Schunck, B.G. (1995) Machine Vision (chapter 5). McGraw-Hill.
[5]
Sharifi, M., Fathy, M. and Mahmoudi, M.T. (2002) A Classified and Comparative Study of Edge Detection Algorithms. Proceedings. International Conference on Information Technology: Coding and Computing, Las Vegas, 8-10 April 2002, 117-120. https://doi.org/10.1109/itcc.2002.1000371
[6]
Zhai, L., Dong, S. and Ma, H. (2008) Recent Methods and Applications on Image Edge Detection. 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, Shanghai, 21-22 December 2008, 332-335. https://doi.org/10.1109/ettandgrs.2008.39
[7]
Raj, D.K. (2017) A Review Paper: On Various Edge Detection Techniques. International Journal for Research in Applied Science and Engineering Technology, V, 534-537. https://doi.org/10.22214/ijraset.2017.8074
[8]
Amer, G.M.H. and Abushaala, A.M. (2015) Edge Detection Methods. 2015 2nd World Symposium on Web Applications and Networking (WSWAN), Sousse, 21-23 March 2015, 1-7. https://doi.org/10.1109/wswan.2015.7210349
[9]
Rashmi, Kumar, M. and Saxena, R. (2013) Algorithm and Technique on Various Edge Detection : A Survey. Signal & Image Processing: An International Journal, 4, 65-75. https://doi.org/10.5121/sipij.2013.4306
[10]
Muthukrishnan, R. and Radha, M. (2011) Edge Detection Techniques for Image Segmentation. International Journal of Computer Science and Information Technology, 3, 259-267. https://doi.org/10.5121/ijcsit.2011.3620
[11]
Mannan, M.A., Ullah, M.A., Dey, U.K. and Alauddin, M. (2022) A Study on Sylow Theorems for Finding Out Possible Subgroups of a Group in Different Types of Order. Mathematics and Statistics, 10, 851-860. https://doi.org/10.13189/ms.2022.100416
[12]
Mannan, M.A., Akter, H. and Ullah, M.A. (2022) Evaluate All the Order of Every Element in the Higher Even, Odd, and Prime Order of Group for Composition. Science and Technology Indonesia, 7, 333-343. https://doi.org/10.26554/sti.2022.7.3.333-343
[13]
Bhadauria, H.S., Singh, A. and Kumar, A. (2013) Comparison between Various Edge Detection Methods on Satellite Image. International Journal of Emerging Technology and Advanced Engineering, 3, 324-328.
[14]
Ansari, M.A., Kurchaniya, D. and Dixit, M. (2017) A Comprehensive Analysis of Image Edge Detection Techniques. International Journal of Multimedia and Ubiquitous Engineering, 12, 1-12. https://doi.org/10.14257/ijmue.2017.12.11.01
[15]
Sharma, S. and Mahajan, V. (2017) Study and Analysis of Edge Detection Techniques in Digital Images. International Journal of Scientific Research in Science, Engineering and Technology, 3, 328-335.
[16]
Maini, R. and Aggarwal, H. (2009) Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing (IJIP), 3, 1-11.
[17]
Rashmi, Kumar, M. and Saxena, R. (2013) Algorithm and Technique on Various Edge Detection: A Survey. Signal & Image Processing: An International Journal, 4, 65-75. https://doi.org/10.5121/sipij.2013.4306
[18]
Othman, Z., Haron, H., Kadir, M.R.A. and Rafiq, M. (2009) Comparison of Canny and Sobel Edge Detection in MRI Images. Computer Science, Biomechanics & Tissue Engineering Group, and Information System, 133-136.
[19]
Wang, J.J., et al. (2017) Associations among Image Assessments as Cost Functions in Linear Decomposition: MSE, SSIM, and Correlation Coefficient. arXiv: 1708.01541。
[20]
Gandhi, S.A. and Kulkarni, C.V. (2013) MSE vs SSIM. International Journal of Scientific & Engineering Research, 4, 930-934.
[21]
Wang, Z. (2011) Applications of Objective Image Quality Assessment Methods [applications Corner]. IEEE Signal Processing Magazine, 28, 137-142. https://doi.org/10.1109/msp.2011.942295
[22]
Sara, U., Akter, M. and Uddin, M.S. (2019) Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study. Journal of Computer and Communications, 7, 8-18. https://doi.org/10.4236/jcc.2019.73002
[23]
Mohammadi, P., Ebrahimi-Moghadam, A. and Shirani, S. (2014) Subjective and Objective Quality Assessment of Image: A Survey. Elsevier.
[24]
Hore, A. and Ziou, D. (2010) Image Quality Metrics: PSNR vs. SSIM. 2010 20th International Conference on Pattern Recognition, Istanbul, 23-26 August 2010, 2366-2369. https://doi.org/10.1109/icpr.2010.579
[25]
Chai, T.F. and Draxler, R.R. (2014) Root Mean Square Error (RMSE) or Mean Absolute Error (MAE). Geoscientific Model Development Discussions, 7, 1525-1534. https://doi.org/10.5194/gmdd-7-1525-2014
