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-This paper presents a study of three artificial neural networks with supervised training and different architectures: network with radial basis function, multilayer perceptron and cellular neural network. These networks were applied to edge detection in aerial and satellite images, for later correlation calculation in spatial domain between these images to simulate the estimation of the geographical position of a unmanned aerial vehicle -UAV. The neural networks results were compared with Sobel and Canny operators.Keywords -Artificial Neural Networks, Image Processing and Air Navigation by Images. IntroductionThe application of UAV stepped up lately due to low operational and manufacture costs compared to conventional aircrafts, don't have crew, among other factors. UAVs are employed in remote sensing, with synthetic aperture radar (SAR) or optical sensors on board, for monitoring cities, human activities or areas of risk such as conflict zones or natural disasters. Radio controlled UAVs usually use antennas with linited reach. In addition, when flying they are subject to possible interferences in the communication process with the control base. Interference in the signal can also occur with UAVs use Global Positioning System (GPS) signals combined with the INS (Inertial Navigation System) for navigation. Possible blocking of GPS signals may also become a limitation to its use. An alternative to overcome these problems is to make use of air navigation systems based real time captured ground information.The use of image processing, computer vision, geoprocessing, and pattern recognition techniques applied to autonomous navigation of UAVs is seen in some studies as in [1], using digital elevation model, in [2] using edge information for the registration of aerial images from satellite image, stereo vision in [3], matching between images in [4], among many.Many papers use ANNs in the navigation system of UAVs, research by [5] shows it. In the literature there are few studies using ANNs in image processing and / or pattern recognition in images in order to help the navigation of UAVs. But the use of ANNs for this purpose can be seen in some studies as in [6], that developed an ANN classify surface in candidate regions for crash in the landing phase of a UAV after the segmentation of the terrain flown. The landing area is chosen using fuzzy rules that use data of the terrain available in the database of the covered areas. The work of [7] also developed an aplication in which ANNs are used in the processing of captured images of the area flown by the UAV. Initially, the texture of the image is extracted by a Gabor filter and an ANN searches the area between some landmarks used for navigation of the UAV, derived from a satellite image. An MLP and a self-organizing map of Kohonen -SOM -were developed for this purpose and their results were compared.
-This paper presents a study of three artificial neural networks with supervised training and different architectures: network with radial basis function, multilayer perceptron and cellular neural network. These networks were applied to edge detection in aerial and satellite images, for later correlation calculation in spatial domain between these images to simulate the estimation of the geographical position of a unmanned aerial vehicle -UAV. The neural networks results were compared with Sobel and Canny operators.Keywords -Artificial Neural Networks, Image Processing and Air Navigation by Images. IntroductionThe application of UAV stepped up lately due to low operational and manufacture costs compared to conventional aircrafts, don't have crew, among other factors. UAVs are employed in remote sensing, with synthetic aperture radar (SAR) or optical sensors on board, for monitoring cities, human activities or areas of risk such as conflict zones or natural disasters. Radio controlled UAVs usually use antennas with linited reach. In addition, when flying they are subject to possible interferences in the communication process with the control base. Interference in the signal can also occur with UAVs use Global Positioning System (GPS) signals combined with the INS (Inertial Navigation System) for navigation. Possible blocking of GPS signals may also become a limitation to its use. An alternative to overcome these problems is to make use of air navigation systems based real time captured ground information.The use of image processing, computer vision, geoprocessing, and pattern recognition techniques applied to autonomous navigation of UAVs is seen in some studies as in [1], using digital elevation model, in [2] using edge information for the registration of aerial images from satellite image, stereo vision in [3], matching between images in [4], among many.Many papers use ANNs in the navigation system of UAVs, research by [5] shows it. In the literature there are few studies using ANNs in image processing and / or pattern recognition in images in order to help the navigation of UAVs. But the use of ANNs for this purpose can be seen in some studies as in [6], that developed an ANN classify surface in candidate regions for crash in the landing phase of a UAV after the segmentation of the terrain flown. The landing area is chosen using fuzzy rules that use data of the terrain available in the database of the covered areas. The work of [7] also developed an aplication in which ANNs are used in the processing of captured images of the area flown by the UAV. Initially, the texture of the image is extracted by a Gabor filter and an ANN searches the area between some landmarks used for navigation of the UAV, derived from a satellite image. An MLP and a self-organizing map of Kohonen -SOM -were developed for this purpose and their results were compared.
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