ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2019 | Volume:7 | Issue:1 |Papers Count:12

Detecting car motion in video frames is one of the key subjects in computer vision society. In recent years, different approaches have been proposed to address this issue. One of the main challenges of developed image processing systems for car detection is their shadows. Car shadows change the appearance of them in a way that they might seem stitched to other neighboring cars. This study aims to propose an optimized method for removing car shadows using entropy and Euclidean distance features. For each pixel, a weight is assigned according to the mentioned features. The weights assigned to shadows and background (asphalt) pixels are very close to each other which enable the background subtraction to remove both of them. The proposed method was evaluated on three datasets based on OA, HR, FAR, MODP and MOTP measures. The method was also compared with both NCC and HSV color methods which are well-known in removing car shadows. The results showed that the proposed methods depending on the type of the index is variable between 3 to 12 percent accurate results.

Natural phenomena and human activities are always changing the earth and Knowing about changad information of the earth’ s surface is becoming more and more important in monitoring the local, regional and global resources and environments. The large collection of past and present remote sensing images made it possible to analyze spatio-temporal pattern of environmental elements and impacts of human activities in past decades. Meanwhile the modern world needs accurate information about these changes for updating Geospatial Information Systems(GIS) and databases. Detecting and monitoring waste sites is one of the most essential parts of urban management and due to the impact of environmental and economic issues for these sites, they must be monitored continuously and in a scientific manner. In this study, detecting the construction debris around Shahryar during a period of time is studied. In this study, images of 2014 and 2015 Landsat8 are used. In this method, and after some pre-processing, using image differencing and spectral indices, change detection map is produced. The overall accuracy of the proposed method was of the order of 96. 41%.

The contemporary world possesses some issues such as urbanism and expansion of cities and these issues which require a specific type of planning. Transit Oriented Development (TOD) planning has emerged as one of the applicable planning methods, which tries to achieve integrated land use and transportation planning. In this study, TOD is conducted at a regional scale using an index measuring TOD level of neighborhoods of the study area in one of the central regions in Tehran city, Iran. The developed index is comprised of some criteria and indicators. Each indicator is computed using spatial analyses in the vector format and then aggregated by Hierarchical Fuzzy Inference System (HFIS). Due to the fact that the research data are related to the year 2005, the activities which have taken place from that year till now are evaluated based on the model output. The evaluation result demonstrates the strengths of TOD planning in the identification of neighborhoods which are in need of development.

Considering the importance of predicting volcano eruption and the large number of volcanic peaks across the world, having enough researches in this field is vital. Based on geodetic researches, surface deformation of the earth in the form of uplift or subsidence in the volcanic region, is an indicator of the magma's movement respectively towards the crater or the exit from the tank around. In this study, using the new method of the fundamental solution, the determination of the deformation field of the Campi Flegrei volcano, taking into account the topographic effect has been considered. Method of fundamental solution is a numerical Meshless method which is used to solve boundary value problems. No need for high computational cost in this method causes it becomes an effective tool in solving many problems in a variety of fields. To verify the efficiency of the method, the resulted displacement field was compared with the Interferometric Synthetic Aperture Radar (InSAR) observations at the Italian Campi Flegrei volcano. The root mean square error was less than 2 mm which indicates a good satisfactory with the displacement field resulting from the observations.

As a process to detect changes in land cover by using multi-temporal satellite images, change detection is one of the practical subjects in field of remote sensing. Any progress on this issue increase the accuracy of results as well as facilitating and accelerating the analysis of multi-temporal data and reducing the cost of producing geospatial information. In this study, an unsupervised change detection method is proposed based on using the image quality parameters; including correlation, spectral distortion, radiometric distortion and contrast between pixels in multi-temporal images. To calculate these indices, a binary mask is used to divide the image into change and unchanged classes. In this paper, to generate the mask, the proposed method applied asymmetric thresholding on signed difference image and in order to produce optimal mask, an iterative algorithm are suggested to find the optimal thresholds. The results demonstrate 5 percent increasing when two asymmetric thresholds are used with respect to use one threshold in absolute difference image. The proposed method is less sensitive to radiometric changes in multi-temporal images. Besides, due to usage optimized threshlding method, this method has less computational cost than random mask optimization methods. Moreover, in comparison with the Otsu thresholding method and Fisher criterion function, the results obtained from the proposed method demonstraste 24 and 21 percent incressing the accuracy, respectively.

Nowadays the urbanization is developing rapidly, and it leads to growing demand for gas; which resulted in denser pipeline network, by the following increase in the pipeline network congestion, accidents will become inevitable. So Pipelines are a remarkable source of hazard for their adjacent society. Usually Indexing system method is used for pipe line risk assessment. This method assesses risk by using an index system, which includes a sum index and a leak impact index. Each index is made up of several sub factors which represent the major cause of failure in the network of gas pipelines. Using spatial analysis, changes in position, status and location of every factor in the vicinity of the pipeline can be checked and it helps to improve pipeline risk assessment. To increase accuracy of result, in this research, an indexing system method, is integrated with abilities of GIS. GIS can also be used as a calculating engine for produce risk scores. The developed model is implemented on a part with 52 km length of greatest Iranian hydrogen sulfide pipeline. The results show that the highest risk rating is 3 and the lowest number is 42. 12 sections of pipeline have low or moderate relative risk due to low leak impact index and lower hazard after gas leakage in these sections. 42 sections of pipeline have lower scores than 10 that show potential failure. In this regard, it is necessary to take measures to reduce risk. The implementation of this research in GIS, increase the accuracy of the risk calculation.

Phase unwrapping is one of the most important parts of InSAR techniques. In order to estimate the grand surface displacements, interferomtric phases modulated between 0 to 2π must be unwrapped. Based on the use of either the conventional method or persistent scatterer (PS), phases will be spread both regularly and irregularly. The phases of PSs can be unwrapped by reducing phases into a regular and continues grid with neatest neighbor interpolation method. In this paper, beside Minimum Cost Flow (MCF) as a global unwrapping method, three local unwrapping methods (Branch-cut, Phase Derivative Variance and Branch cut-Phase Derivative Variance) are introduced as well. These conventional unwrapping approached are implemented on an irregular interferogram processed from Sentinal1A satellite images acquired over the Sirjan basin. At the end, the results of these approaches are assesed with unwraped phase which is resulted in a conventional interferogram unwrapped with MCF method.

Reducing Emissions from Deforestation and Forest Degradation (REDD) is a climate change mitigation strategy employed to reduce the intensity of deforestation and GHGS emissions. In recent decades, drastic land use changes in Mazandaran province caused a substantial reduction in the amount of Hyrcanian forests. The present research based on objectives of REDD projects paid to identify of forest cover changes in a range of Marzan Abad and Kojour Districts in the Mazandaran province using of Landsat satellite images from 1984, 2000 and 2014. In this study, for the first time in Iran, using similarity weighted instance– based learning (SimWeight) approach, forest cover changes modeling was performed, and for validation, statistics of relative operating characteristic (ROC), ratio of hits/false alarms and figure of merit was applied. Finally, using voluntary carbon standard (VCS) methodology CO2 emissions for the 30 next years (until 2044) was calculated. The results showed that forest cover decreased about 4008 hectares and 3635 hectares during 1984-2000 and 2000-2014. The validation results indicated that ROC equal to 0. 95, the figure of merit equal to 26 percent and the ratio of hit/false alarms equal to 82 percent reflects high accuracy of the model. Eventually, REDD project's implementation results designated that under the baseline scenario about 705336 tCO2e will release into the atmosphere over the 30 next years that REDD project can prevent the release of 491697. 91 tCO2e. With respect to increasing deforestation in Hyrcanian forests and their important role in the mitigation of climate change, using the methodology offered can be estimated and predicted land cover changes and the impact of REDD projects on reducing GHGS emissions, and the REDD results can be used to complete the Project Design Document (PDD) of Clean Development Mechanism (CDM) in the country.

In recent decades, population growth and progress of technology have shaped large and compact urban settlements. Existence of huge transportation systems and developed urban infrastructures are among the most important properties of modern cities. In spite of prompt transit and facilitated daily activities, development of transportation systems causes many problems, including traffic, air and noise pollutionand so on. Noise pollution as one of the most important human health threats can cause annoyance, sleep disturbance, decreased learning ability and even cardiovascular problems and blood pressure in long time. Sound is caused by a wide range of activities and processes. Road traffic, railways, airplanes landing, workrooms, industries and communicate places are the most important noise sources. Awareness of the noise emission status in urban environments and the identification of people at risk are primary steps that should be taken in planning as a basic information to reduce vulnerability. Currently, trains running along railways, after traffic jams, are ecoming the crucial source of noise pollution. This is especially important in a large city such as Tehran, which crosses the rails a long way in residential areas. In this study, a spatial approach was used to model the noise pollution caused by railway transport in Tehran. To this end, the RMR standard was used to calculate the noise level generated in the source of sound (rail path). The noise emission and its attenuation in the environment was also modeled based on the method presented in ISO 9613-2. According to the results, the railway noise range in the studied area varies from 43 to 88 dB. The comparison of the estimated noise level with the measured values indicates a 3. 5 dB RMSE. The overlapping of the noise map with the demographic data indicates that around 200, 000 people are exposed to noise pollution of more than 70 dB and about 730, 000 are exposed to noise pollution greater than 60 dB. Obviously, in the absence of proper residential insulation, the health of a large population of residents adjacent to the railway is at serious risk.

Researchers and urban administrators have often considered the routing problem as one of the fundamental phases in developing hazard management systems. In this research, a routing problem is investigated and analyzed by proposing an enhanced metaheuristic algorithm based on biogeography. In this problem, the production planning, inventory management, and distribution planning have been considered, and the purpose is to minimize the total costs of production setup, inventory holding, and distribution of relief products. Then, to avoid the problem of premature convergence to local optima and to improve the efficiency and convergence rate of the algorithm on large-scale and constrained problems, a new optimization algorithm based on biogeography with a new migration operator is proposed. With regard to the instances of routing problems, the performance of the proposed algorithm is compared to other methods based on the running time, convergence speed, robustness, best and average of the results and statistical superiority. The statistical assessment verifies the efficiency improvements and obtaining better results by the proposed strategy in tackling the temporal relief routing task.

Least square matching (LSM) is one of the most accurate image matching methods in photogrammetry and remote sensing. The main disadvantage of the LSM is its high computational complexity due to large size of observation equations. To address this problem, in this paper a novel method, called fast least square matching (FLSM) is being presented. The main idea of the proposed FLSM is decreasing the size of the observation equations to improve the efficiency of the matching process. For this purpose, the pixels in the matching window are ordered using a special robustness measure. Then, a specific percent of the pixels with the highest robustness is selected for matching process. The phase congruency and entropy measures are used to compute the proposed robustness measure. The proposed FLSM method was successfully applied to match various synthetic and real image pairs, and the results demonstrate its capability to increase matching efficiency. The matching results show that the proposed FLSM method is three times faster than standard LSM method.

Although tidal observations which are extracted from coastal tide gages, have higher accuracy due to their higher sampling rate, installing these types of gages can impose some spatial limitation since we cannot use every part of sea to install them. To solve this limitation, we can employ satellite altimetry observations. However, satellite altimetry observations have lower sampling rate. According to spatial limitation in installing tide gages and lower rate of satellite altimetry observations, we need observation as along with gathered information which can solve those two main margins. Buoy observations not only for its higher accuracy and sampling rate, but also because of its exclusive features can let us observe further coastal regions to record sea level observations. In this study, buoy observations are analyzed using Least Square Harmonic Estimation (LS-HE) method. According to this, important frequencies in those data can be extracted. This process is equally important in Tidal modeling as well as prediction. Tidal modeling and prediction are based on frequency which is derived from observations. In this contribution, time-series data of 57 buoy stations from 2005 to 2017 are processed and a list of important frequencies is prepared. In the following, the comparison between tidal prediction modeling extracted from buoy and tide gage observation was made by using this frequency list. Tide prediction of all the stations during a month was made according to important frequency list extracted in this study. The average RMSE for predicted data in buoy stations has been about 6 cm. Finally, to validate buoy’ s data, comparison between buoy and tide gage data was made which\ represents about 9 cm difference in sea level prediction.