An edge detection algorithm of remote sensing images based on fuzzy sets

Liu, Yi; Xuequan, Chen

doi:10.1109/icccas.2004.1346344

Cited by 3 publications

(7 citation statements)

References 5 publications

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“…EGR is an indicator of the concentration of the pixels in the lower and higher gray values after enhancement and its higher values resulted in a more effective edge detection algorithm. 28 After this step, the iteration will be continued until the EGR reaches its peak and starts to decrease. The enhanced image is the one with the highest value of EGR.…”

Section: Methodsmentioning

confidence: 99%

“…Edge detection method, introduced in Ref. 28, was proposed for satellite remote sensing images and was tested for four types of high resolution images, including Landsat 7, Ikonos, QuickBird, and SPOT 5 images, where the results show better performance than the traditional Pal. King algorithm 36 , 37 and Canny edge detector.…”

Section: Methodsmentioning

confidence: 99%

“…22,23 Image enhancement and edge detection can be useful in remotely sensed imagery due to various uncertainties (e.g., clouds and their shadows, fogs, and smokes) affecting its quality. 7,9,20,[24][25][26] In the majority of edge detection methods applied on satellite remote sensing images, the detection process is implemented on the images corrupted by some types of noise, which are added manually, 27 or the results are evaluated and interpreted visually, 28 since there is a variety of degradation effects, such as atmospheric effects, clouds and their shadows, atmospheric aerosols, fogs, smokes, and radiation from other energy resources, including land and atmosphere, which may remarkably decline the quality of the satellite remote sensing images and make them blurry. For instance, edge detection in coastal regions is not adequately possible due to some reasons such as sea waves or shallow water in some places, which may confuse edge detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…29,30 One of the best groups of the methods introduced for image enhancement and edge detection is based on the fuzzy logic theory. 11,28,[31][32][33][34][35] Fuzzy enhancement and edge detection on satellite remote sensing images were first introduced in Refs. 36 and 37, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…After detecting the edges using this method and evaluating their feasibility, the final results were compared with the results of the other edge detection methods, such as fuzzy edge detection algorithm proposed by Liu and Chen in Ref. 28, and the Canny edge detection algorithm as a widely used edge detection algorithm 39 , 40 …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Rahimzadegan

Sadeghi

2016

J. Appl. Remote Sens

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.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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Section: Methodsmentioning

confidence: 99%