نشریه علوم و فنون نقشه برداری
سال سوم شماره 1 (پیاپی 9، امرداد 1392)

GNSS kinematic techniques provide precise coordinates from short observations time span, or even while the receiver is moving. The accuracy of stop and go kinematic methods is lower than static method, while the required time for processing is shorter. Augmenting the GNSS kinematic equations by constraints increases the degrees of freedom and the accuracy of the estimated unknowns. These constraints could be derived from geometric relations between receiver positions during the observations. In this contribution the constraint which is added to the system is a circle. Application of this known constraint increases the accuracy of the estimated unknowns. Meanwhile the designed and the estimated radiuses will identical by the hundredths of a millimeter level (for simulated RINEX observations). In this paper stop and go kinematic positioning with virtual observation by known constraint (the center and radius of the circle assumed to be known) and unknown constraint (the center and radius of the circle assumed to be unknown) was carried out. The results of these two constraints were similar, in other words if the center and radius of the circle are unknown, prior to constraint implementation, one can compute these parameters with estimated GNSS positions. Kinematic positioning with true observations was carried out applying constraint. The results state that the constraint increases the accuracy of processing, especially in horizontal component. Again the computed radius from processed coordinates was equivalent to the designed one.

In geoid computation and gravity field modeling, downward continuation of the gravity field functional is done by solution of the Able-Poisson integral equation. Since the Able-Poisson integral equation is one type of Fredholm integral equations, this equation is ill-posed and need to be regularized to find the best solution. In this paper, 6 usual regulation methods are compared together on simulated data in a case study in south of Iran. The result showed the Koch- Koshe and Philips-Tikhonov with L-curve Criteria methods have the best solution in case study area (CoastalPars).

Image matching is an essential task in digital photogrammetry. The fully aoutomatic matching of convergent close range image is a difficult task due to possible significant geometric distortions in these images. In this paper an efficient and fully aoutomatic matching method for convergent close range images is presented. The proposed method consists of three main steps. In first, the initial feature extraction and matching is performed using ASIFT (Affine-SIFT) algorithm. In the second step the epipolar geometry based on fundamental matrix is estimated using genetic algoritm to eliminate most of outliers in initial match features. Due to the defect of epipolar geometry to remove all outliers, in third step of proposed method a new approach to remove reminding outlier from previous step is proposed. In this method cluetering process is performed by k-Means algorithm and followed by a consistency check in the local affine transformation model for each cluster. The experiment results using both synthetic data and various sets of convergent close-range images show that the proposed algorithm provides reliabile and acuurate matching.

Receiving updated information about the network of roads from high resolution satellite imagery is a crucially important issue in continuously changing developing urban regions. Considering experiences in road extraction and also exploiting distributed evolutionary computational approaches, in this paper a new framework for road map updating from remotely sensed data is proposed. Three main computational entities of antagent, seed extractor and algorithm library are designed and road map updating is performed through three main stages of verification of the old map, extraction of possible roads and grouping of the results of both stages. Extracting corresponding pixels to each road element in the map, an object level supervised classification or any available road verification algorithm from the library capable of producing a road likeliness value is applied. Since road extraction is a simple and also a complex problem, more comprehensive algorithms are chosen from library iteratively by antagents so the decision about verification and rejection of each road element is finally made. Ant-agents facilitate choosing road elements and moving of ant agents via stigmergic communication by pheromone cast and evaporation. The proposed method is developed and tested using GeoEye-1 pan-sharpen imagery and 1:2000 corresponding digital vector map of the region. As observed, the results are satisfactory in terms of detection, verification and extraction of roads and generation of the updated map specifically in case of inspection of main roads. Besides, some missed road items are reported in case of inspection of bystreets and alleys specially when situated at the margin of the image. Completeness, correctness and quality measures are computed for evaluation of the initial and the resulted updated maps. The computed measures verify the improvement of the updated map.

Due to their numerous advantages, the applications of spatial services are growing rapidly. Gaining such advantages requires proper infrastructures such as processing power and main memories. If the resources match the requirements, the services execute optimally. At present, the infrastructure has been prepared by the servers at the traditional data centers in the form of centralized or distributed architectures. However, such architectures have fix capacity and are inflexible. As the received requests to the server are not constant, the proper prediction of processing sources is difficult. When requests are more than (at the peak time) the capacity of the resources, the system cannot react to the request. On the other hand, if the resources are more than what is needed, the investment is wasted. The solution may be found in cloud computing. Cloud computing technology adds the flexibility and scalability to the servers by applying concepts of service orientation, and virtualization. In order to examine the capability of cloud computing, in this article we deployed a WebGIS on the basis of cloud computing, and evaluated it. This WebGIS has optimum route finding on the basis of Dijkstra algorithm. Many optimizations have been done in the algorithm to perform the calculation of optimum routing in larger road networks. Our system is deployed with the help of Microsoft cloud computing service (Windows Azure) and MapGuide Server in. NET framework. Finally by applying a load testing in the traditional client-server architecture and recommended architecture, the system is assessed. The comparison is done from the point of flexibility and scalability views. At last, the results shown that the suggested architecture of cloud computing is more efficient than traditional clientserver architecture. Also form the economic point of view it is much better than the traditional systems.

Different application of GPS position time series in geodetic and geophysical studies such as plate tectonics, glacial isostatic rebound, crustal deformation and earthquake dynamics requires proper assessment of the time series. A functional model for GPS time series consists of a linear trend, periodic signals with annual and semiannual periods and probabilistic offset. The undeterministic effects can best be described as a noise. The correct detection of offset requires a proper estimation of noise and the covariance matrix of the data. Towards this end, the least-squares variance component estimation is used. It is shown that how a correct analysis of the noise components can affect the offset detection method. Ignoring colored noise of GPS time series degrade the offset detection power. We first use the univariat time series analysis. To increase the offset detection power, the multivariate analysis is then recommended. In univariat analysis, only one of the coordinate components is used, while in the multivariate analysis all three components, which is assumed to have the same structure of noise and offsets, are simultaneously used. In this paper, some simulated daily time series of positions during 8 years are used. Finally, offsets having different size and positions are located through them and detected successfully, using the multivariate analysis.

With the rapid development of spatial data production, collection, and processing techniques large amounts of data, which cover the same geographical extent, are available. These huge amounts of data motivate the researchers in geospatial analysis domain to create multiscale database in order to update the datasets and perform the multi-scale analysis. In the core of any spatial multiscale database is the data matching process. In this paper, a new algorithm is proposed for data matching which is implemented and evaluated in two major steps. Firstly, after pre-processing, the search and identification of candidate objects in the two datasets are carried out based on the topological features. Secondly, the best pair of corresponding objects is selected using the least squares method and comparing the geometric features of linear objects between the candidate object selected in the previous step. The algorithm is evaluated on the roads maps of scales1:25000 and1:50000 produced by national cartography center of Iran (NCC).The results show that the algorithm is successful and 94.2% of the objects were correctly identified as the corresponding pairs.