Browsing by Author "Flesia, Ana Georgina"

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    Fisher Vectors for PolSAR Image Classification
    (SADIO, 2018-09) Redolfi, Javier; Sánchez, Jorge; Flesia, Ana Georgina
    In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive semidefiniteness property of their complex counterpart. Based on this observation, we derive an FV from a mixture of real Wishart densities and integrate it with a Potts-like energy model in order to capture spatial dependencies between neighboring regions. Experimental results on two challenging data sets show the effectiveness of the approach.
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    Learning to Detect Vegetation Using Computer Vision and Low-Cost Cameras
    (IEEE, 2020-02-27) Redolfi, Javier; Felissia, Sergio Francisco; Bernardi, Emanuel; Araguás, Gastón; Flesia, Ana Georgina
    A problem of current agriculture is the large amount of agrochemicals used to boost production due to their cost and the environmental pollution they cause. A partial solution to this problem consists in developing selective spraying techniques through the measurement of a green index that allows the selection of the precise amount of pesticide to be applied according to the specific conditions of each part of the field. Some of the problems of the existing systems are the inability to discriminate between types of vegetation and to pinpoint its location, since they only detect general patches of vegetation. In this work, we introduce a system prototype capable of measuring the presence of vegetation in an area using low-cost devices combined with current computer vision techniques. The system allows to generate a mask with the presence of vegetation in a certain area and it is also capable of distinguishing between different materials unlike current methods, which only allow to distinguish between green and non-green areas. The presented method opens the door to future research which can allow distinguishing between crops and weeds to make an even more selective application. The output of the system can be used also to design another type of weeding method that is not based on the application of agrochemicals.
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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.