Development of the iterative edge detection method applied on blurred satellite images: state of the art

Majid Rahimzadegan; Behnam Sadeghi

doi:10.1117/1.JRS.10.035018

26 August 2016 Development of the iterative edge detection method applied on blurred satellite images: state of the art

Majid Rahimzadegan, Behnam Sadeghi

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 035018 (August 2016). https://doi.org/10.1117/1.JRS.10.035018

Abstract

This paper aims to implement an iterative fuzzy edge detection (IFED) method on blurred satellite images. Some degradation effects such as atmospheric effects, clouds and their shadows, atmospheric aerosols, and fog remarkably decline the quality satellite images. Hence, some processes such as enhancement and edge detection in satellite images are challenging. One group of methods that can deal with these effects is fuzzy logic methods. Therefore, IFED method was applied in this work on the subimages of the Ikonos, Landsat 7, and SPOT 5 satellite images, contaminated by aforementioned effects. Such as most FED methods, IFED has two components: enhancement and edge detection. In this context, a six-step iterative method, using the if-then-else mechanism, was implemented on the images to perform fuzzy enhancement, and subsequently, edge detection was done. To evaluate the merit of the enhancement and select the best number of iterations, edge gray-value rate criterion was applied. The peak signal-to-noise ratio (PSNR) is applied for the quantitative evaluation of the IFED method. The results of IFED, in comparison with some prior edge detection methods, showed higher PSNR values and a high performance in the edge detection of the earth features in the blurred satellite images.

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Majid Rahimzadegan and Behnam Sadeghi "Development of the iterative edge detection method applied on blurred satellite images: state of the art," Journal of Applied Remote Sensing 10(3), 035018 (26 August 2016). https://doi.org/10.1117/1.JRS.10.035018

Published: 26 August 2016

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