Change detection is a process of detecting differences with the objects or phenomena which are observed in the different time intervals. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1985 -2013). The area observed in this study is the region of mountain Zlatibor (Serbia) with its surroundings. The methods represented in this study are vegetation indices differencing, Supervised classification and Object based classification. These methods gave different results in term of land cover area, and it is generally concluded that supervised classification gave the most accurate results with the images of medium spatial resolution. The results of this study can be used for urban and environmental planning. All information lead to conclusion that the surface under the forests is reduced for about 4% (or about 1000 ha) while the built up area has doubled (grown about 600 ha) during the examined period. The results also highlights the importance of change detection techniques in land cover for the areas that are developing rapidly, such as Zlatibor study area.

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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.

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This paper presents the results of a research study where ground penetrating radar (GPR) was successfully used to reveal the remains of the Württemberg-Stambol Gate in the subsurface of Republic Square, in Belgrade, Serbia. GPR investigations were carried out in the context of renovation works in the square, which involved rearranging traffic control, expanding the pedestrian zone, renewing the surface layer, and valorising existing archaeological structures. The presence of the gate remains was suggested by historical documents and information from previous restoration works. A pulsed radar unit was used for the survey, with antennas having 200- and 400-MHz central frequencies. Data were recorded over a grid and two three-dimensional models were built, one for each set of antennas. The grid was the same for both sets of antennas, therefore the two models could be compared. Several horizontal cross sections of the models were plotted, corresponding to different depths; these images were carefully examined and interpreted, paying particular attention to signatures that could originate from the sought archaeological structures. Reflections coming from the gate remains were identified in both models, in the same region of the survey area and at the same depth; the geometry, size, and layout of the gate columns, as well as of other construction elements belonging to the gate, were determined with very good accuracy. Based on the GPR findings, archaeological excavation works were carried out in the region where the foundation remains were estimated to be. The presence of the remains was confirmed, with various columns and side walls. This case study demonstrates and further corroborates the effectiveness and reliability of GPR for the non-invasive prospection of archaeological structures hidden in the heterogeneous subsurface of urban environments. In the opinion of the authors, GPR should be incorporated as a routine field procedure in construction and renovation projects involving historical cities.

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This paper presents a comparative study of two algorithms for detecting and analyzing the characteristic shapes of reflection obtained as a result of Ground-Penetrating Radar (GPR) scanning technology. The first algorithm is a sub-array processing method that uses direction-of-arrival algorithms and the matched filter technique; this approach is implemented in SPOT-GPR (release 1.0), a new freeware tool for the detection and localization of targets in radargrams. The second algorithm, APEX, is based on machine learning and pattern recognition techniques and it allows finding the coordinates of apexes and further characteristic points of hyperbolas in radargrams. Both software solutions are implemented in MATLAB environment. As a first step, we compare the accuracy of our algorithms when applied to synthetic data, calculated by using the open-source finite-difference time-domain simulator gprMax; the scenarios are two concrete cells hosting different metallic and dielectric targets. Then, we compare the accuracy of our algorithms when applied to experimental data, recorded over district heating pipes in a trench, with known geometry and depth of the pipes. For the latter scenario, we have also generated a gprMax radargram, matching the geometry and scanning settings of the real one; both algorithms are tested on this synthetic radargram, as well. Overall, both algorithms perform well and rather uniformly in localizing the targets. The accuracy of the algorithms is at centimeter level, which is sufficient in most applications.

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