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• W4313147855 endingPage "5438" @default.
• W4313147855 startingPage "5372" @default.
• W4313147855 abstract "Edges are distinct geometric features crucial to higher level object detection and recognition in remote-sensing processing, which is a key for surveillance and gathering up-to-date geospatial intelligence. Synthetic aperture radar (SAR) is a powerful form of remote-sensing. However, edge detectors designed for optical images tend to have low performance on SAR images due to the presence of the strong speckle noise-causing false-positives (type I errors). Therefore, many researchers have proposed edge detectors that are tailored to deal with the SAR image characteristics specifically. Although these edge detectors might achieve effective results on their own evaluations, the comparisons tend to include a very limited number of (simulated) SAR images. As a result, the generalized performance of the proposed methods is not truly reflected, as real-world patterns are much more complex and diverse. From this emerges another problem, namely, a quantitative benchmark is missing in the field. Hence, it is not currently possible to fairly evaluate any edge detection method for SAR images. Thus, in this paper, we aim to close the aforementioned gaps by providing an extensive experimental evaluation for SAR images on edge detection. To that end, we propose the first benchmark on SAR image edge detection methods established by evaluating various freely available methods, including methods that are considered to be the state of the art." @default.
• W4313147855 created "2023-01-06" @default.
• W4313147855 creator A5012818617 @default.
• W4313147855 creator A5040849460 @default.
• W4313147855 date "2022-07-18" @default.
• W4313147855 modified "2023-10-16" @default.
• W4313147855 title "SAR image edge detection: review and benchmark experiments" @default.
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