2025-06-102016https://hdl.handle.net/20.500.12272/13218The automated detection of coasts, riverbanks, and polynyas from syn thetic aperture radar images is a difficult image processing task due to speckle noise. In this work we present a novel Fuzzy-Wavelet framework for bordeline region detection in SAR images. Our technique is based on a combination of Wavelet de noising and Fuzzy Logic which boost decision-making on noisy and poorly defined environments. Unlike most recent filtering-detection algorithms, we do not apply hypothesis tests (Wilcoxon-Mann Whitney-G0) to label the edge point candidates one by one, instead we construct a fuzzy map from wavelet denoised image and extract their borderline. We compare our algorithm performance with the popu lar Frost–Sobel approach and a version of Canny’s algorithm with data-dependent parameters, over a database of real polynyas and coastline simulated images under the multiplicative model. The experimental results are evaluated by comparing Pratt’s Figure of Merit index of edge map quality.pdfeninfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/waveletsFuzzy LogicSAR imagesedge detectionenvironmental sustainability engineeringinfo:eu-repo/semantics/articleKarim Alejandra, Nemer Pellizahttp://creativecommons.org/licenses/by-nc-nd/4.0/http://dx.doi.org/10.1080/23311916.2016.1216725