نشریه مهندسی نقشه برداری و اطلاعات مکانی
سال چهارم شماره 4 (پیاپی 16، آذر 1392)

Since there are different applications of radar in various sciences doing research and study on radar is necessary. one of the radar image applications is DEM generation. one of the essential DEM generation steps is image matching. radar image matching is more complex than matching on optical images. one of the most serious problems in SAR image matching is speckle noise. besides the noises, some geometric distortions like shadows, foreshortening, and layover also increase the complexity images matching. in this paper, a SAR image is firstly filtered by a speckle suppression algorithm and then a Harris algorithm is used to extract feature points. Then descriptor is introduced and local matching is performed. Furthermore,we compute the first image point extracted to the second image point extracted displacement with multi quadric equation. Finally, we use least square image matching for image matching improvement. Proposed method for on TerraSAR-X images of the area JAM-IRAN was tested. The result shows that our proposed method is superior to the Local matching in terms of accuracy and number of matched points.

Spatial Resolution is one of the most important geometric parameters of aerial and space imagery. This key parameter introduces the minimum distance between two distinguishable adjacent objects in the image. For accurate measurement of spatial resolution, some targets with standard geometric formats are used. These targets can be either artificial targets or applicable objects in the image to measure spatial resolution. In this paper, a method for measuring spatial resolution of UltraCamd images with Siemens star target, as one of the most usable artificial target, is introduced. Result shows that the measured spatial resolution of UltraCamd images is slightly less than its nominal spatial resolution for this sensor.

In most of the applications that LiDAR point cloud is used, segmentation is a preprocessing stage which is of considerable importance. The accuracy of this process has a great impact on the final result of the desired purpose. One of its main applications is 3D building reconstruction, for which different roof facets must be extracted first. So, for the LiDAR point cloud segmentation, in this paper, Mean Shift algorithm is used. Mean Shift algorithm is a non-parametric process which doesn’t require prior knowledge about data and also about clustering parameters such as the counts and the centers of the clusters. The input data is as an irregular LiDAR point cloud, also it is assumed that the buildings’ roofs are composed of some planar facets. In order to evaluate this method, using 2D map, several different buildings were selected and the results were evaluated both qualitatively and quantitatively. The performance of proposed method in dealing with different types of buildings was significant.

The goal of an optimum site selection problem is to select a location with special use based on different criteria. Generally, optimum site selection problems have complex structure. One of the most important problems in cities is optimum site selection in Fire Stations that have more importance due to saving lives and properties. In this study, be tried to close this problem to reality by considering 13 effective criteria in optimum site selection for fire stations. Also by integrating GIS, Fuzzy Reasoning Systems (FRS) and Bee Colony optimization method, a new reasonable method proposed to solve optimum site selection problem. GIS used in evaluation and analyze candidate locations, FRS used to calculate rate of proximity between candidates and ideal expert's locations and bee colony used to optimize candidate locations on time. Proposed locations outcome from this method made increasing in service area from 69% to 95.2% that cause more servicing to urban.

A growing number of cell phones, digital cameras, PDAs and other hand-held devices are equipped with georeferenced data collection technologies, made it possible for ordinary people to collect spatial data, which are then shared and disseminated on the internet using web map services. It has led to a huge source of spatial data termed as Volunteered Geographic Information(VGI) by Mike Goodchild. Volunteered geographic data are constantly being added, edited or removed by the users, so different versions of the same data may exist. It is very common in geographic communities to confront with several versions of the same data and select the relevant one based on the quality parameters stored in the metadata. However, in case of VGI, the users are not experts and do not necessarily have high spatial knowledge. Therefore, a system administrator decides on behalf of the users to select and publish one of the produced data as the best, which is in most cases the latest one, because they believe that if the data is not correct, someone will improve it later. However, this is not the right assumption. Quality of spatial data depends on different parameters such as spatial accuracy, attribute accuracy, completeness, logical consistency and updateness. Therefore, the best data may differ from an application to another.In this article, we propose providing the VGI users with the spatial data quality parameters through simple cartographic representation and let them decide on selecting the appropriate dataset for the application in hand. The users select the desired quality parameters as well as the visual element (e.g. color, intensity, style, thickness, etc.) to classify the desired parameters. All the datasets are represented by the selected visual element based on their metadata information, which help the users to visually compare them and select their appropriate dataset. In addition to determine the quality of each data set, instead of comparing them with the ground truth data they’ll be compared with each other to define the relative quality of them.The proposed approach has been implemented for a case study where 16 versions of 2D map were produced using different data collection methods (i.e., surveying, GPS and digitization) and with different spatial data quality parameters, limited here to spatial accuracy and completeness. An ArcGIS extension was developed to display the datasets based on the visual classification elements as well as spatial quality parameters selected by the user, which eventually help the user to visually compare the existing data and select the appropriate one for his/her certain application.

Typically the random errors exist in every observed parameter as an undesirable part of measured observation which following the normal distribution function (NDF) in statistical components analysis as a satisfactory assumption. Generally those random errors straying from NDF characteristics are being considered as blunders or outliers throughout the set of observations which would effect on the sensitivity analysis negatively. This paper presents application and comparison of Baarda method as a conventional statistical methodology and Fuzzy approach in order to determine the outliers in two set of data including three dimension (3D) points of ground Laser Scanner so-called cloud points as well as a set of random simulated 3D cloud points. Although there are several methods to analyze the random errors and outliers but it is still necessary to find more optimized solutions especially for 3D cloud points such as ground Laser Scanner observations and or performing an improvement to the previous ones in terms of accurateness and preciseness. However Baarda method eliminates the outliers as soon as finding them but Fuzzy method deals mostly with the residuals and observed errors in the adjustment computations procedures which depends critically on statistical tests outputs. The results show that the detected outliers using Baarda method sequel to or more than what Fuzzy method whether for the real cloud points of Laser Scanner or the random 3D simulated cloud points discovered. Moreover, Baarda method is acting quite similar to the Fuzzy method’s results in low number of points approximately while the comparison presented that Fuzzy method approached to the acceptable value of 1 through ameliorating adjustment computations in larger set of points quantity step by step. Besides, the future capabilities of Fuzzy approaches to detect the outliers in different levels of observation quantity are discussed.

Based on the improvement of the geodetic techniques for the observation of the fault mechanism, it became very important to determine the stability and accuracy of the measurements. Following the new technologies such as GPS and high quality observations, many studies on tectonic problems can be initiated and improved. However, the quality and reliability of data obtained and results deduced from these studies depend on the location of stations, in other words, the design of geodetic networks, measurements techniques and observation errors. The purpose of this study is the optimum designing for the geodynamic networks which estimates the most accurate values for the geometric parameters of the fault. For this scope, a simple and one dimensional model for a strike-slip fault is used and number of stations is fixed around the fault and it is assumed that we observe the deformations with these station positions correctly, and, using simulations, we checked the relationship between the accuracy of deformation model parameters based on errors (correlated noise and monument instability) and then investigate the sensitivity of the parameters according to the number of the stations and the distance from the fault.