This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.
The study of vegetation cover, forests, orchards or vineyards and crops through satellite techniques is increasingly promoted as a result of facilities they offer. Since 2010 until today, it is launched over 50 satellite platforms, delivering images of the Earth's surface with different spectral, spatial and radiometric characteristics. New satellite images such as RapidEye, WorldView2 or WorldView3, with its high spatial and radiometric resolution, prevents the use of the standard image analysis techniques (such as supervised or unsupervised), and involves the use of modern methods in image analysis, such as object base image analysis. A number of methods and techniques for processing and analysis of satellite images are developed to increase the precision of the working, given the diversity of vegetation structure analysis and expected results. This study aimed to analyze the capabilities of object-oriented image analysis (OBIA) for recognition forest and vineyard areas. OBIA is automated process of object extraction by modelling of human visual system for image interpretation. The basis for classification process is object, which is created according to the set of characteristics. In object-oriented approach classification description is based on classification rules including spectral characteristics, size, shape, as well as content and texture information. Analysis is done on multispectral imagery of high and very high spatial resolution. Represented results show the usefulness of RapidEye and WorldView2 images as well as importance of classification based on OBIA. Object-oriented image analysis (OBIA) method based on satellite imagery has facilitated the recognition forest and vineyard areas with high accuracy.
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