JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2014 | Volume:3 | Issue:3|Papers Count:9

Integral Equations such as one-step inversion based on the first derivative of the ellipsoidal Poisson’s integral, for transformation of gravity values on the Earth’s surface to the gravity potential on the reference ellipsoid are used for geoid determination. One of the main problems in numerical solution of integral equations is the resolution of input data. In this study, we have shown that the required resolution of the input gravity data on the Earth’s surface for correct one-step inversion depends on the height of the computational region, the fact that if overlooked can cause totally wrong results. For detect that the resolution of input data is sufficient, we study the behavior of the integral kernel and change the integral kernel to overcome the adverse effect of insufficient resolution of the input gravity data are the novel contributions of the study. For numerical tests, we have choose a test area with real gravity data in the west of Iran and The numerical results approve the success of our proposed method to solve the problem of insufficient resolution of the input gravity data for correct one-step inversion.

It is important to answer the question of how occurrence of an earthquake can trigger another? An earthquake happening is accompanied with fault or fracture in the earth's crust. For this purpose, it is necessary to examine failure criteria in elastic environment of earth’s crust that is Coulomb failure criteria. Coulomb stress change analysis has been applied in numerous seismically active regions of the globe. These studies demonstrate categorically that the locations and rates of future earthquakes are affected by the static stress changes due to nearby historical earthquakes. In this study, Coulomb stress interaction between the fault systems in Iran has been studied. Parameters of earthquakes with magnitude above 6 that have occurred in this region from 1900 to 2001 have been collected from various sources in order to calculate Coulomb stress. The results indicate that more than 80% of the occurred earthquakes from 2001 to 2013 in the region of increased Coulomb stress caused by the past 100 years earthquakes have the rate of 0.01 to 5. Also, the calculation of Coulomb stress changes on the strike-slip, stretching and normal fault pages with optimized geometry shows that the faults which have the same direction as optimized geometry, are prone to rupture and cause earthquake as the results of increased Coulomb stress caused by earthquake and are considered as areas of high risk for future earthquakes.

An ongoing challenge in high-resolution satellite image (HRSI) processing has been the establishment of accurate epipolar geometry over the entire HRSI image area, which is a pivotal step of any stereo-image processing, including 3D topographic mapping. However, the push broom camera which is used by most high-resolution satellites does not produce straight epipolar lines. Furthermore, in contrast to the well-known frame cameras, the epipolar curve pair does not exist for the entire stereo image area. These properties make it difficult to establish the epipolar geometry of a push broom camera, resulting in a limited accuracy of the epipolar image resampling. In this study, a new method of epipolar curve pair determination and epipolar image resampling of space borne push broom imagery, based on the popular RPC (Rational Polynomial Coefficients), is proposed.

The main goal of this research is to develop a knowledge-based recommendation system for real estate marketing. The system is capable of handling both spatial and attribute data of the properties. In general, there is no specific and stable relation between the parameters of the properties. In other words, we cannot extract any domain for the values of other parameters from the values of any given parameter. Therefore, the case-based method is used for the development of the recommendation system. The hedonic method is used for the property valuation and the geographical weighted regression method is used for extracting the coefficient of hedonic function. The valuation is based on the information of sold properties, considering different parameters such as construction year, number of building unit, frontage and distance from parks, schools, hospital, etc. In this system, information regarding for-sale properties are stored in the sale properties. Similarly the information regarding the preference and requirement of real estate buyers are stored in buyer profiles. Comparing this two, three of the most similar properties to the preferences of the buyer are recommended by the system. The system is tested using the information of sold properties in the “Vanak” region of district 3 of Tehran. Evaluation of the system, shows that the recommended properties are actually the most fitted ones to the requirement of the sample buyers.

Mixed pixels are one of the main problems in the remote sensing data classification. There are various reasons including limitations of sensor spatial resolution or costs of data acquisition that may cause the image accuracy to be less than desired quality. A similar problem may be in comparing different spatial resolution images of one scene. Increasing the spatial resolution in such cases is necessary. Different methods have been proposed to increase the spatial resolution with maximum accuracy. These studies considered proportion of sub pixels and locating their positions. These methods and their problems will be examined in this study. Conventional methods are reviewed. And a new method for increasing the spatial resolution will be proposed to resolve some of the weaknesses of existing methods. This new algorithm is proposed for restoring sub pixels in the hyper spectral images. This algorithm is based on mixture of pixels for creating a proper search table for restoring sub pixels. The spatial and pattern properties of neighboring pixels are considered in this search. There is no need to use soft classification information. Testing algorithm on real data will show its performance and capability in remote sensing applications. Simulation results on real data show that proposed method can increase percentage of correction classification at least 15% relative to hard classification.

Because of using the different polarization of electromagnetic wave in Polarimetric imagery, it provides a rich source of information from the several aspects of targets. Recently, Polarimetric images as a powerful and efficient tool have been interested to identify the various objects in the complex geographic areas. In order to extracting information, classification of Polarimetric image has an important effect. Support Vector Machines (SVMs) due to their operation based on geometrical characteristics and robustness in high dimensional space, are considered as a suitable case for classification of Polarimetric images. However, the performance of SVMs classifier is strongly influenced by its parameters. Therefore, the optimum values for SVMs parameters should be determined to achieve SVMs classifier with maximum efficiency. Traditional optimization techniques because of computational complexities in the large search space usually trap in local optimum. Thereby, it is inevitable to apply Meta -heuristic Algorithms which performe exploration and exploitation to obtain global optimum. In this paper, the potential of Genetic, Bees and Particle Swarm Optimization (PSO) Algorithms as powerfull techniques in determining the optimum SVMs parameters are evaluated. Comparing the results, demonstrates the superior performance of PSO Algorithm in terms of classification accuracy and speed of convergence.

Digital elevation models (DEMs) are one of the most important data for various applications such as hydrological studies, topography mapping, image ortho rectification, 3D images generation, extraction terrain parameters, disaster management, and etc. A digital elevation model can be derived from numerous techniques with different elevation accuracies. In photogrammetric techniques, a DEM can be extracted from stereo satellite images through many processing steps. A satellite imagery based digital elevation model is called ASTER GDEM ver2 was released on 2011 at a spatial resolution of 1 arc-second. This model was evaluated by differencing with other reference DEMs in order to investigate the quality and accuracy parameters over different land cover types. In this paper, an accuracy assessment of ASTER GDEM ver2 dataset with SRTM and local elevation model over the bare area of a port in southwest of IRAN is presented. This study investigates DEM’s characteristics such as systematic error (bias), vertical accuracy and outliers for these three datasets. The accuracy measures for the assessment of the height differences between each DEMs can be calculated based on the usual (Mean error, Root Mean Square Error, Standard Deviation) and the robust (Median, Normalized Median Absolute Deviation, Sample Quantiles) descriptor. The results demonstrated that there is a large negative elevation bias of approximately -4.5 m and -2.8 m of ASTER GDEM ver2 against the SRTM and local DEM. The median of the differences between GDEM and local DEM is about -3.7 m which is a robust measure to prove the existence of systematic shift between the two data. The RMSE measured for elevation differences between GDEM and two other DEMs is same, but the standard deviation GDEM and local DEM differences are higher than the value of this parameter between GDEM and SRTM. The accuracy measures NMAD of GDEM against the local DEM and SRTM are 5.5 m and 5.9 m, respectively. On the other hand, about 68% of the GDEM and local DEMelevation differences are in [0, 7.5] m, while this values are in [0, 9] m for GDEM and SRTM elevation differences.

The increasing availability of location-acquisition technologies like GPS has enabled people to record their location history and sharing it on internet. Using this capability in web based social networks and considering the shared spatio-temporal data as edge that connects users of social networks to spatial data layer, has resulted geo-social networks. The dimension of location brings social networks back to reality, bridging the gap between the physical world and online social networking services. With the huge amount of frequently updated information from all around the world, geo-social networks are rich sources for data mining which can be used in providing different efficient location based services and improving user's social relations structure. In this paper we aim to mine interesting locations, attractive travel sequences and expert users in a given geospatial region according to multiple users' shared checkin data that has been modeled with a hierarchical uniform framework. At the end, by using discovered knowledge, a service that will offer location for visiting according to the user's spatio - temporal query has been represented.

By development of remote sensing sensors, hyper spectral remote sensing images are now widely available for monitoring the earth’s surface by using high spectral resolution and dimension. However, this large dimension not only increases computational complexity but also degrades classification accuracy. Dimensionality reduction is a major issue to improve the efficiency of the classifiers in hyper spectral images. The common ways for dimensional reduction is feature extraction. Ideally, the reduced representation has a dimensionality that corresponds to the intrinsic dimensionality of the data. There are a wide range of methods in intrinsic dimensionality estimation and dimensionality reduction of hyper spectral Images in literatures. In this paper, we discuss and compare five intrinsic dimensionality estimation (IDE) techniques for hyper spectral dimensionality reduction. We investigate the performance of these techniques for IDE on hyper spectral images, and compare their performances for supervised image classification purpose. These techniques include; Eigen value estimator (EV), Maximum likelihood estimator (ML), Correlation dimension estimator (CD), Packing number estimator (PN) and geodesic minimum spanning tree (GMST) estimator. The K-Nearest Neighbor (KNN) classifier used for supervised image classification. The variety of the distance metric was used and compared in this classifier. The most useful and practical methods for reduction of dimensionality Principal Component Analysis (PCA) and Independent Components Analysis (ICA) were used in output of these IDE techniques. This study presented a review and comparative study of techniques in IDE. Then feature bands used in supervised classification with variety in parameters usage.