Unsupervised fuzzy-wavelet framework for coastal polynya detection in synthetic aperture radar images. Issue 1 (31st December 2016) - Nemer, Karim Alejandra; Pucheta, Martín Alejo; Flesia, Ana Georgina
Abstract: The automated detection of coasts, riverbanks, and polynyas from synthetic 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 denoising 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- G 0 ) 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 popular 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. In almost all test images our algorithm outperforms the standard algorithms in quality and speed.