UTN- FRC -Producción Académica de Investigación y Desarrollo - Artículos

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/2453

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    Unsupervised fuzzy-wavelet framework for coastal polynya detection in synthetic aperture radar images
    (Cogent Engineering, 2016) Pelliza, Karim Alejandra; Pucheta, Martín alejo; Flesia, Ana Georgina
    The 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.
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    Optimal canny’s parameters regressions for coastal line detection in satellite-based SAR images
    (2020) Nemer Pelliza, Karim Alejandra; Pucheta, Martín Alejo; Flesia, Ana Georgina
    Canny’s algorithm is a very well-known and widely implemented multistage edge detector. The extraction of coastal lines in space-borne-based synthetic aperture radar (SAR) images using this algorithm is particularly complicated because of the multiplicative speckle noise present in them and can only be used if Canny’s parameters (CaPP) are chosen appropriately. This letter introduces a methodology for computing functional forms for the CaPP, using functions of the image characteristics through a system that combines artificial neural networks (ANN) with statistical regression. A set of CaPP functional forms is obtained by applying this method on synthetic SAR images. Pratt’s fig- ure of merit (PFoM) is used to measure the performance of them, obtaining more than 0.75, on average, in the 14 400 synthetic SAR images analyzed. Finally, this set of formulas has been tested for extracting coastal edges from real polynyas SAR images, acquired from Sentinel-1.