JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2015 | Volume:4 | Issue:4|Papers Count:21

Surveying has great improvements in data collection techniques in last decade.one of these techniques is laser scanner. With that method we can collect 3D data automatically. Investigating of the error sources in TLS measurements is rather complicated due to a large number of influencing factors that are quite interrelated. Thus calibration is an important issue in these devices. Several models have been proposed to improve the accuracy of the laser scanners data until now. Each of these models includes some physically parameters and some empirically parameters which have been produced by observation of residuals diagram, in this paper a parametric model based on the internal structure of laser scanner is presented for calibrating these devices. This model compared with another models shows that due to having just physical parameters and not empirical parameters it can be used for a variety of TLS instruments.

Using 3D spatial data causes efficiency in urban planning and faster decision making. Within the last years, such terms like Volunteered Geographic Information (VGI) and User Generated Geographic Content (UGGC) appeared, and produce a completely new phenomenon in public collecting spatial data and define a new source of data. Adding 3D data to VGI is an important step. It seems, there is not an interoperable method for collecting and sharing 3D data in the current VGI. The purpose of this study is to provide guidelines in VGI systems for preparing 3D building models. Therefore in this study, "Wall", "Roof", "Door" and "Window" were considered as the fundamental building structural elements. Then we have developed a hierarchical classification based on ontology, for describing the interaction of these elements. A methodology for collaborative development 3D building models is suggested. Within the framework of our work, users are able to add 3D information in VGI interactively. On the other hand, is looking for ways to increase non-expert users’ participation. For this purpose, a user can upload pictures of buildings that are used for texture, then as well as digitizing aerial images, digital images are superimposed on the buildings. It causes that data entry to be adequate.

Modeling and evaluating land suitability is one of the main prerequisites for urban land use planning. Integration of GIS and Spatial Multi Criteria Group Decision Making tools have been considerable role in this research field. In this study, modeling residential land suitability has been carried out using Fuzzy Spatial-Group Multi Criteria Decision Making, TOPSIS-OWA and GIS in Kermanshah region includes Kermanshah, Harsin, Sahneh, Songhor and Kangavar cities. Ten criteria maps include climate, topography, type of Land, land use, land cover, road accessibility, energy accessibility, latitude, crowd population, zoning earthquake hazard and water resource accessibility have been selected and modeled by OWA and IOWA methods based on the opinion of 4 experts. Then the results of first step have been combined by Fuzzy TOPSIS after weighting by considering 3 environmental, economic and social factors. The results of the proposed model show best locations for expanding residential area that satisfy most aforementioned criteria. This result indicates that integration of multi criteria decision making methods and fuzzy phrases by analytical GIS tools, considering the knowledge of a group of experts, can used as suitable approach in evaluating land use. After omitting the protected areas, according the results in the case study area, 7.2 percent of the region is very suitable, 32.6 percent is suitable, 34.2 percent is slightly suitable and 7.5 percent is not suitable.

Urban land use planning which is one of the main components of urban planning typically defined as a multi-objective planning problem in optimal use of urban space and existing facilities. Among numerous land use maps, urban planners are usually interested in choosing the map which is contiguous to the optimal land use map of an interested vision. Reference point multi-objective optimization algorithms provide possibility of introducing the optimal values for different objectives as a reference point and producing optimal solutions near to reference points. In this study, the implementation and efficiency of Reference-Point-Nondominated Sorting Genetic Algorithm II (R-NSGA II) for urban landuse allocation is investigated and a method for chromosomes coding is proposed. Maximizing compatibility of adjacent land use, land suitability, accessibility to roads and main socio-economic centers, and minimizing resistance of land use to change are defined as the main objectives. Then the optimal values of objectives were introduced to the algorithm as reference points. Consequently, planners will be able to select within proposed land use maps according to their priorities. The results of land use allocation modeling for Shiraz city in 2011 indicate that the decision maker is able to choose a better decision with more reliability comparing to situations with a single solution. This achievement indicates proposed model ability for simulation of different scenarios in land use planning.

Edges are one of the salient properties of the image in which they have important information of the image and represent shape characteristics of the objects. Edges are important features due to the fact that the human visual system uses a preprocessing step for edge detection. The majority of the classical mathematical algorithms for the edge detection, such as Gradient, Laplacian and Laplacian of Gaussian operators are based on the derivative of the original image pixels. In the remote-sensing imagery, because of the high rate of changes, these edge detection operators perform weakly in correct detection of the feature boundaries and keeping their consistency. In order to solve these problems, this research presents a new technique using Shannon entropy based on Imperialist Competitive Algorithm (ICA). In this method, firstly a piecewise thresholding is used to identify the threshold of different parts of the image, and then the area boundaries are extracted using the Shannon entropy based on the selected threshold. According to obtained results, selection of thresholds have a high influence on the final results, Based on this, ICA optimized method is used in this research. In order to evaluate the performance of algorithm, the results from the proposed technique are also compared with the results obtained from Canny, LOG, Sobel, Roberts, Ant colony optimization edge detector and other Entropy edge detectors. The results show that the proposed method presents higher reliability in detecting the edges of different digital images.

Interferometric synthetic aperture radar (InSAR) time series is a technique to evaluate earth surface deformation over large areas. One of the InSAR time series algorithms is small baseline subset (SBAS) method that has been successfully used for monitoring deformation. Noise assessment and spatiotemporal evaluation of deformation time series is an important factor in understanding and interpreting deformation in the study area. In this paper, we used SBAS interferometry method for extracting deformation time series caused by land subsidence in the Mahyar plain in Isfahan province of Iran. Noise structure of the deformation time series is then estimated with using multivariate w-test statistics. This assessment leads to estimate parameters of deformation time series realistically. Moreover, the results show that overall-time series do not provide new information about deformation in study area, according to spatial correlation among neighbor pixels.

3D deformation analysis is a considerable issue in Earth's crustal deformation studies. In this research, 3D deformation analysis is addressed using isoparametric approach and also developing 2D mode to the 3D one. Since in geodetic networks, some components of the baselines (usually height difference of the points) are smaller than other components, extension of 2D isoparametric method to 3D mode will be a n ill-posed problem which results in unstable solutions. Such this property makes use of regularization approaches inevitable. Use of regularization approaches makes the solution biased. In this regard, in this research for the first time, 3D deformation analysis is formulated such that the discussed problem is switched to a well-posed problem resulting stable solutions. Parameters of such coordinate system are determined through the process of strain tensor components determination. Kenai Peninsula located in south central is selected as the study area. Instability of the 3D deformation analysis has already been proved and demonstrated. Resulted solutions by the extended approach of this research corroborate the fact of maximum compression being in center part of this region emphasized by other researches. Resulted strain tensor is the first unbiased determination of strain tensor in this region computed using a non-element approach. Comparison of resulted solutions to the solutions obtained by 2D deformation analysis (a similar approach) in this region demonstrates that magnitude of vertical deformation effect on horizontal principal strains of the strain tensor is as large as 1.7 micro strain. This value will be a large amount for the horizontal mentioned components in 2D mode. Hence, disregarding vertical components effects and kinematic investigation of in 2D can lead to a significant bias in the solution. Nevertheless, some overall properties of deformation in region such as maximum compression in center part of this network is observable by 2D deformation analysis. Moreover, studying magnitude of bias due to the regularization demonstrates inappropriate adoption of regularization parameter can cause a significant bias in the obtained results. In this regard, use of already known deformation properties in this research, such as maximum compression in the center part, is not a suitable approach for regularization parameter adoption.

Cardiac arrest is a condition where the heart rate disappears completely from the heart is not pumping blood. In spite of the fact that the majority of the cardiac arrest cases of homes or hospitals will be reported, about 20 percent of the cardiac arrest cases will be occurred in public places. Several factors in the incidence of cardiac arrest are impressive. These factors include environmental, personal interests, context information are patient person profile data. Due to the fact that in the preparation of maps of these factors, use of geographic information system and spatial analysis. But due to the volume of the top layers of requirements, to analyze the underlying data accurate and efficiently take advantage of the powerful methods, such as the optimization algorithm has been suggested. In this study, the context information used including the environmental context information (such as land-use, distance from hospitals, elevation, the economic status of the area, and reported cases of cardiac) and person profile information (such as age, smoking and disease status) has been. For the evaluation of the study area and the dangerous places in the incidence of cardiac arrest from swarm intelligence algorithms include the ACO and PSO were used. Reason of this choice more easily using these methods, compliance with real world issues, the better modelling of uncertainly and so on. Due to the lack of data required within our country, the proposed model is typically run for public places was State of Pennsylvania Petersburg. The results of the research confirms the impact of context information in the abundant occurrence of cardiac arrest. For example changing in the people context information, is to lead to a change in the map class about 98 percent.

Today's digital elevation models have many applications in various fields including engineering projects and management of natural resources and etc. One of these applications is a topographic correction in InSAR to find the amount and rate of displacements. The purpose of this paper is to compare the effects of two digital elevation models with a resolution of 30 m and 90 m in order to obtain displacement rates from radar images. Persist Scatterer and Small Baselines methods were used to compute the displacement rate in the region. Processing was performed with both models SRTM and ASTER. The maximum difference between the results from two elevation models is observed in areas with a high elevation difference. In both methods, the number of persist scatterers in the case of model ASTER is less than model SRTM. In areas with low elevation differences, the results of two elevation models are very similar to each other. But in areas with high topography, the low resolution elevation model does not have the ability to deliver results with appropriate accuracy. In PS method there are 0.2 mm difference in maximum and 1.1 mm in minimum of displacement rate field and in Small Baselines method, these rates were 4 and 1 mm respectively. In order to better evaluate the results, six points in the region were examined. The maximum difference between the results was 4 mm. This difference is significant at the ten percent level of confidence. As a result, in areas of high topography, it is necessary to use the more accurate digital elevation model to achieve higher accuracy.

Speckle phenomenon is appurtenance of radar image nature that is caused accuracy reduction in interpretation, classification, segmentation, etc. over these images. Researchers have tried to decrease this phenomenon in radar images. One of the speckle phenomenon reduction methods in the radar images is spatial domain filters. Main methods of the spatial domain filter use thresholds for classifying type of targets, then speckle phenomenon decreases based on type of target. Using threshold in images for target classification is a method with great error. Therefore, clustering method is used for target classification in this research. In proposed method, firstly, speckle phenomenon of radar raw image decreased by using Mean filter. Afterwards, K-Means clustering method implemented over the filtered image with various cluster numbers. Optimal cluster number in case study determined using Davis Bouldian index. Finally, filtered image was produced in a decision level with the clustering and the raw radar images. Results of the proposed method were compared with other method including Mean filter, Lee filter, Enhanced Lee filter, Gamma filter and Median filter by using radiometric and edge preservation indexes. Results show that the proposed method has higher accuracy than the other implemented methods based on the mentioned indexes.

Iran has always been exposed to dust storms because of its climate, geographical location and proximity with the neighbor’s desert such as Iraq, Syria and the Saudi. Hence, detection of dust phenomenon is a critical issue facing our world. In this research, the dust storms which occurred in Eilam and Khuzestan provinces during 2005 to 2012 were detected using multispectral technique and applied criteria in global models were customized for the study area. For this purpose, a decision tree algorithm is utilized for distinguishing cloud and dust and then the dust from ground surface is separated using the same reflectance behavior. First, appropriate training data for three classes of cloud, dust over dark and bright surfaces and clear sky is selected. Secondly, the reflectance behavior of pixels in the mentioned classes is analyzed. In the next step the best bands for the detection of dust pixels are chosen and the improved decision tree is recommended for the study area. Finally, the accuracy of the proposed algorithm is evaluated and compared with the previous algorithms using criteria such as visibility and weather codes from the meteorological data of the study area. The results show if the improved method is used the accuracy would increase. Eventually, if the Normalized Difference Dust Index (NDDI) Indicators and Ln (b1) are used for dust detection over bright surface, the accuracy will be 58 percent. Moreover, for dark surfaces the accuracy of 53 percent is achieved using the NDDI and BTD (BT20-BT31).

Hyperspectral images, with high spectral resolution, have caused vast progress in remote sensing extensions. One of the most important applications of these images is agriculture and forest. The purpose of this research is improvement in classification of various vegetation types over Botswana region by using combination of target detection algorithms and Hyperspectral image. In the first step, target detection algorithms implemented over the preprocessed Hyperspectral image. In the second step, information of target detection algorithms was combined by using the proposed method. Results of the proposed method were implemented for different windows size. The best overall accuracy of the method was 96.16 percent for 3*3 windows size that its overall accuracy has approximately improved at least 8 percent and uttermost 20 percent with respect to the results of target detection algorithms.

Move through the environment is an inseparable activity of human life. People need to know where they are and how they can arrive to their intended destination at the right time. Among several navigation aids (e.g., maps and satellite navigation systems), they may use their spatial knowledge (i.e., cognitive map) for this aim, especially in familiar spaces. Human spatial behaviour is complex due to its dependence on various external and internal factors. Factors such as age, gender, education, IQ, environment design, affect human spatial decisions. Investigating such a complex system is too difficult, or sometimes, simply impossible to directly deal with directly. To model this complex process, one first requires knowledge of the scientific principles of human spatial cognition and its elements. A shortcoming of cartographic maps is that they are limited to a top view representation of the space. Therefore, they only provide metric information and topological relations, while map information can turn in to stable and accurate spatial knowledge by frequent using and simultaneous interaction with environment. One of the proposed ideas in order to make navigation aids consistent with human cognition, is enrichment of cartographic maps with the elements of spatial cognition. This enriching makes person to interact with the environment through his cognition while moving by navigation aids. In order to practical using of this idea, it is necessary to evaluate these maps to assess its effectiveness in the process of spatial knowledge creation and improvement. This study, first, presents human spatial cognition and describes its elements by focusing on urban environment characteristics and the way human observe and perceive it. Then, on the basis of these principles, an agent-based model is proposed to present human mental process while observing and perceiving environment, in a more understandable way. In this model, the various stages of human cognition including spatial data storage, integration and upgrading spatial knowledge and retrieval of this knowledge for navigation, is simulated for a pedestrian in urban environment. The proposed model is used to evaluate a map enriched with elements of human spatial cognition.

Currently, in the field of optimization, ant colony algorithm has been implemented successfully on a wide variety of optimization problems. This algorithm is inspired by the real life of ants to find the shortest path from nest to food. This behavior of ants is closely similar to the inverse problems in geophysics which try to find the best solution for the unknowns in observation model. Therefore, this idea is applied for solving linear inverse problems. The aim of this article, is inversion of the gravity data in a linear form, it means that with constant geometric parameters, the physical parameters to be modeled. For examine the performance of this algorithm, firstly the algorithm has been tested on artificial complex T and L model. This method is applied for artificial models with and without noise. Outcomes show that for inversion by the use of ant colony algorithm, there is no need to separation of interferential anomaly and it is possible to use it for a combination of density contrast. Finally, the propose method for the measurement of regional gravity data Gotvand Dam is located in the province have been used. The results of the inverse of the data, and large diameter holes with depth in the region. As a result of dam construction in the study area, according to the regional geological information would cause serious environmental problems.

Relative radiometric normalization often used in multi-temporal satellite image analysis, especially land use change detection. In this paper, IR-MAD transformation has been reviewed and a new method has been developed based on this transformation and artificial neural networks, also. The proposed method is implemented on multi-temporal Landsat TM satellite images captured in 1989 and 2010. Study area is located in Tabriz. According to Linear combination of multi-temporal satellite images, transfer the images to another space and iterative process of IR-MAD transformation, the transformation has led to independent method of statistical noise and atmospheric conditions and Used in this study for change detection and selection radiometric controls point. The capability and flexibility of ANN in approximation of nonlinear and linear continuous functions in the hybrid space has led to the networks used for modeling of relationship between radiometric controls point in multi-temporal satellite images. Evaluation metrics in this paper, include root mean square error, T-test and F-test. The results show that the proposed method increases the accuracy and performance relative radiometric normalization. The proposed method has increased. Root mean square error in all spectral bands than IR-MAD and raw data respectively 0.11 and 8.13%.

Earthquake is one of the most destructive events which menace human life. Therefore, researchers and scientists are trying to find any signals before this natural disaster and maybe to predict it. According to previous efforts and researches done by different scientists, one of earthquake precursors could be unusual changes in electron content before the occurrence of large earthquakes (greater than 6). In this study, the changes in ionospheric electron density on 11 August 2012 Ahar-Vaezeghan and on 16 April 2013 Saravan earthquakes are quantified. For this purpose, single-layer ionospheric model using GPS data of continuous stations in NW and SE Iran was considered. In this paper, an integrated wavelet analysis methodology is proposed to detect and report any relationship between ionospheric total electron content (TEC) anomalies and seismic activities. Cross-wavelet analysis as a mathematical tool represented the relationship between abnormal ionospheric total electron anomalies and sun activities. There are some limitations due to high solar activity, magnetic storms and the lack of final satellites orbit at the real time, which should be considered in this method. In order to do a better study to detect ionospheric TEC anomalies related to seismic activities, it can produce a time series of these anomalies by using GPS sites distributed in active faults zones, DEMETER satellite and iono-sound data.

Noise removing and radiometric correction is one of the challenging in Hyperspectral image processing. The one of these errors is striping which is presented in most of the remote sensing imagery. The destriping methods include statistical and filtering approaches. In the most of these algorithms, the structural information also removed after destriping. The presented method is combined wavelet-FFT filter in order to remove stripe artifact problem. In the first step, the original image is wavelet decomposed and subsequently, the bands containing the stripe information (vertical detail) are FFT transformed to remove the stripe errors. The visual assessments, as well as quantitative estimation of energy loss of the result show the capabilities and the performance of the purposed method in order to destriping. Also the result shows all structural features, which are different from stripes are optimally preserved and despite the statistical methods, the purposed algorithm doesn’t need the neighborhood information.

Natural period of vibration (or natural frequency) is one of the structural dynamic characteristics. It does not depend on seismic loading and depends only on buildings' specifications. There are some analytical and experimental methods for determination of natural periods. In this paper, the natural periods of a three-story building are studied using free vibration test. This building seismically induced by initial displacement in X and Y directions. The structural responses are measured by strong video camera and two-frequency GPS device mounted on the roof. Image processing was used for determination of natural periods (or natural frequencies). The tests were repeated for two directions. Analytical methods were done for controlling of the experimental results. For example, the natural frequencies in X direction were determined 1.07, 1.09 and 0.93 Hz using GPS, image processing and analytical methods respectively. The natural frequencies for Y direction were also obtained 1.13, 1.19 and 1.08 Hz respectively. Comparing the results of the image processing and GPS measurements indicate good convergence results of the two methods in determination of the dynamic characteristics of structures. The results from analytical method also are the same as the experimental results approximately. The small error is due to simulation of the model in analytical software.

Geosensor networks are new and developed generation of wireless sensor networks in location-based part for detecting, reviewing, monitoring, tracking, and processing of environmental phenomena. Due to existing limitations in geosensor networks, especially limited energy source, in this paper the decentralized computing system is used in which in-network processing and minimizing information transport reduces considerabily the energy consumption of network. In this paper, some algorithms are designed based on decentralized computing system, which responds to snapshot queries for extraction of topology relation between regions. In these algorithms, only local information of each node and achieved neighbors information are used. They can deduce topology relations between regions. The main challenge in the applications of geosensor networks is its discrete information space. In this research, boundary structures, boundary nodes, boundary cycle, and boundary orientation are used. Afterwards, the topology relations of containment, adjacency, and overlay are extracted in this discrete space. Containment and adjacency algorithms are the foundation for the overlay algorithm. In the containment and overlay algorithms, all three boundary structures are used while boundary orientation is not required at the adjacency algorithm. Implementation of these algorithms is simulated and the achieved results are explained.

One of the most important applications of hyperspectral data analysis is either supervised or unsupervised classification for land cover mapping. Among different unsupervised methods, partitional clustering has attracted a lot of attention, due to its performance and efficient computational time. The success of partitional clustering of hyperspectral data is, indeed, a function of five parameters: 1) the number of clusters, 2) the position of clusters, 3) the number of bands, 4) the spectral position of bands, and 5) the similarity measure. As a result, partitional clustering can be considered as an optimization problem whose goal is to find the optimal values for above-mentioned parameters. Depending on this fact that which of these five parameters entered to the optimization four different scenarios have been considered in this paper to be resolved by particle swarm optimization. Our goal is, then, finding the solution leading to the best accuracy. It should be noted that among five different parameters of clustering, both similarity measure and the number of clusters have been considered fixed to prevent over-parameterization phenomenon. Investigations on a simulated dataset and two real hyperspectral data showed that the case in which the number of bands has been reduced in a pre-processing stage using either band clustering in the data space or PCA in the feature space, can result in the highest accuracy and efficiency for thematic mapping.

Highway design includes many process and sub process from planning to real construction. Before precise engineering and road design, early stage of road location should be done. This is stage that basic criteria should be met with minimum negative environmental, social and economic impacts as well as road engineering criteria. This stage without attention to environmental, social and economic patterns of region might design a route that is ideal from engineering aspects, but might has negative impacts on this region, and so led to people dissatisfaction and sometimes stopping project. Therefore, since decision making in planning and highway construction involve many factors and stakeholders, is complex. Using multi-criteria methods and spatial analysis can reduce decision maker problems in route selection. The goal of this paper, using GIS spatial analysis and multi-criteria decision making method by considering environmental, social-economic and technical criteria in route selection. In this paper, fuzzy group AHP for weighting criteria (with comparing criteria by transportation experts and public) and VIKOR method for rating route alternatives. The results of implementation for Gilangharb-Sumar project shows that selected route using decision making methods and spatial analysis corresponds to determined route by consultant engineering company.