نشریه علوم و فنون نقشه برداری
سال دوم شماره 3 (پیاپی 7، بهمن 1391)

Deformation survey using geodetic techniques for large buildings and landslides monitoring can be performed using multiple epoch’s method. The first step is the design of a reliable monitoring network. To compute the displacement, a coordinate system (datum) is to be established. Such a system is defined over a few points that are assumed to be stable during the campaign. We therefore require an efficient method for stable points’ detection. The simultaneous adjustment method is presented in the present contribution. L1 norm minimization method and global congruency test are the other two methods for this purpose. We aim to study the performance and sensitivity of simultaneous adjustment to identify the stable and unstable points using simulated data at four epochs. This method is then compared with the L1 norm method and the global congruency test for simulated and real data at two epochs. The results illustrates that increasing the observation epochs augments the ability of deformation analysis and stable points detection for simultaneous adjustment. In addition, simultaneous adjustment provides superior results over small displacements compared to the other two methods.

This paper proposes an optimal mathematical model for updating road sides from high resolution satellite images. Based on this model, The Ant Colony Optimization (ACO) approach is first used to establish a pheromone matrix that represents the edge information at each pixel position of the image. Next the line plausibility map is used as the external energy in a snake model to identify and extract roads sides of image. Snake is a parametric curve which is allowed to deform from some arbitrary initial location toward the desired final location by minimizing an energy function based on the internal and external energy. The approach is validated by a series of tests on high resolution satellite images.

Automatic extraction of objects from data and images has been a topic of research for decades. This paper proposes an improved active contour model focusing on building extraction from color aerial images and LiDAR data. An active contour is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. Based on the radiometric and geometric behaviors of buildings, the active contour model is modified in two aspects: the criteria for the selection of initial seeds and the external energy function. The proposed active contour model associated the new height similarity energy factor and regional similarity energy as well as Gradient Vector Flow (GVF) attract the active contour approaching the object contours efficiently. Compared with traditional active contour model, this algorithm can converge to the true building contours quicker and more stable especially in complex urban environment. Investigation of results expressed that, buildings extracted from complex suburban area have 96% of shape accuracy, 95.9% of overall accuracy and 84% k-Factor.

To discover a suitable geological structure for Petroleum exploration, development of three-dimensional 3D modeling of the structure, are required. The 3D visualization provides a comprehensive picture of geometric characteristics of the geological structure. Such model is generally made by integration of surface geological data and satellite images which are draped on digital terrain models of a specific area. Up to now, these surface data including dip and strike of structural planar features would be measured and collected during field observations in the study area. Generally, the fieldworks are exhausting and consume more time and money as well. Moreover, some locations are not accessible to collect required data of the geological structure. To solve these problems, the remote sensing techniques e.g. GPS/ INS/ STAR TRACKER besides satellite cameras provide powerful, quick and inexpensive tools for collecting the required surface geological data. Here, we use cartosat1 (IRIS P5-streoalongtrack) satellite images with rational coefficient for determining the surface data, strike and dip. The most advantages of the method are that there is no need to apply any ground control points for providing surface data. Therefore, it is no required to arrange any field observation plan. To evaluate the study results, we test the suggested method using previous geological measurement published on the geological maps for two different areas, located in the Zagros range and the Albourz Mountains. The results indicate the accuracy in a range of few degrees for dip and strike measured by the suggested method compared to the direct geological measurements. These differences may be due to local solution in geological maps and global solution in our method and good point is that geologist needs global solution.

Particle Swarm Optimization is one of the Swarm Intelligence Algorithms that is inspired of birds’ behavior. This algorithm has been usually used in continuous optimization problems but not much mentioned in discrete issues. In this study, by evaluating the weakness and strength points of different versions of DPSO, we tried to develop a new type of this algorithm so that it can have a high capability in solving the discrete problems. The efficiency of this new algorithm has been successfully examined on the optimization for the problem of temporary settlement after earthquake. Temporary settlement after earthquake is one of the significant issues in crisis management that doing it correctly will result in the decrease of physical and financial losses originated from aftershocks. In order to optimize the temporary settlement, important targets such as minimizing the population replacement and homogenous distribution of individuals relative to the capacity of safe places should be considered. After the mathematical definition of two mentioned objective functions, the weight for each of them has been calculated by means of AHP. Then the linear combination of objective functions has been made as the final target function to be optimized by weighted average method. Evaluating the proceeds of calibrated algorithm, data from part of region 7 of Tehran’s municipal were collected. Finally the repeatability test was used to evaluate the precision of the algorithm and the obtained results are representative of the precise proceeds of this algorithm in allocating individuals into the safe areas.

A fuzzy predictive model is The atmospheric gases, aerosols and clouds scatter and absorb solar radiation and can modulate the reflected radiation from the earth. Therefore, the atmosphere contributes to the at-sensor radiance. So, one of the requirements for extracting reliable spectral information is to first compensate for atmospheric effects. Therefore, Atmospheric correction is a most important preprocessing step required in many remote sensing applications. There is a plethora of methods available, from simple to complex, which addresses correction and calibration issues. Some of the most accurate and severe methods are physically-based models, which require atmospheric data coincident with remote sensing data acquisition. Preparation of these data is difficult and expensive. In this study, a physical based algorithm proposed which provide partial information about atmosphere from GPS radio occultation profile. The method is provided to two global steps: in first step, atmospheric parameters are calculated using radiative transfer model. Then, in second step, calculated parameters are used for atmospheric correction. The results of the atmospheric correction process are evaluated by checking the level of spectral information using entropy criterion and comparing the computed at-surface reflectance with in-situ surface reflectance measurements. The results show the improvement of atmospheric corrected surface reflectance based on GPS radio occultation. Also, the information content criterion shows higher spectral content in images resulting from the proposed method.

Points sampled from an object that contains information about the object. These points don’t have topological information or special arrangement and are merely a set of points with coordinates. One of the most popular approaches for reconstructing boundary and extracting the central axis, using Delaunay triangulation and Vorony diagram for sampled points. The main problem with this approach is high sensitivity to noise and distortion. Therefore it is necessary to perform a filtration step aimed at removing excess branches of central axis. However, the filtration method as a preprocessing step or post processing cause to topological and geometrical changes in central axis. In this study, a new method for reconstructing boundary and extracting the central axis will be presented. As a practical example, the algorithm presented in this study will be used to extract the street center lines. Conceptual structure and the results of this study indicate that the stability, flexibility, high accuracy, even when there is noise and distortion border.