ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2014 | Volume:2 | Issue:2|Papers Count:6

In any organization, the allocation of buildings and offices space to departments and employees is a challenging task. In organizations office space allocation problem, employees are allocated to buildings and offices space so that some objectives are satisfied and the optimize allocation is obtained. So if this problem is modeled well, some advantages will be achieved. For example: increasing synergy among the employees, using of space optimally, and decreasing costs. Because of this problem is an optimization problem based on different constraints, so at the first we attempt to specify these constraints by experts. It is possible that under some circumstances, the appropriate combination of criteria for creating of fitness function does not take place, so we attempt to use multi-objective optimization with pareto solution. We use multi-objective bee colony algorithm to reach this objective. In this method, we have a set of optimal solutions instead of an optimal solution. This set has optimal solutions that each of them is optimal and does not have any priority to other solutions. In this situation, user can select an optimal solution from that set with consideration of existing conditions. Bee colony algorithm can solve continues and discrete problem and has simple operations. In this research, this algorithm has ability to modeling a problem and responding to the demands in appropriate time.

In this paper an efficient method for automatic road detection from high-resolution multi-spectral IKONOS images is presented. The system includes four main steps: In the first step the input image is segmented into road and background classes using K-means clustering and then some misclassification pixels in road binary map are removed using a median filter. In the second step, angular texture shape descriptors (mean, compactness and eccentricity) are driven for every road pixel in road binary map. In the third step, these descriptors are introduced into a fuzzy inference system. In the fuzzy system each descriptor is introduced as a linguistic variable with Gaussian membership functions while their parameters are set automatically according to statistical properties of each descriptor. Also, some fuzzy if-then rules are established. By using the centroid defuzzification, road network is distinguished from other spectrally similar classes (shadows, buildings, parking lots and etc). Then, road network is completed by connecting road pixels together and removed of small paths. In the last step of system evaluation, obtained results are compared with manually extracted road network and some accuracy assessment parameters are computed. The conventional maximum likelihood classification (MLC) is also implemented and the same accuracy assessment parameters are determined for comparison. Preliminary results show the effectiveness of the methodology of this paper in both resembling the desired results of road networks and achieving a good automation level. Furthermore, it outperforms MLC to high extent.

Persistent Scatterer Interferometry (PSI) was presented in order to overcome the limitations of conventional Interferometry through selection of so-called Persistent Scatterers (PS) with coherent scattering behavior over time using a priori deformation model. The algorithm later developed as StaMPS (Stanford Method for Persistent Scatterers), is able to select the PSs without using a pre-defined deformation model and extract the deformation even in areas lacking the corner reflectors. However, if the deformation rate is too high, Nyquist sampling criterion required for temporal unwrapping will not be fulfilled. As a result, StaMPS will underestimate the deformation rate. In this paper, an efficient approach is presented to improve StaMPS performance for estimating high deformation rate. PS pixels are firstly selected using the method proposed in StaMPS. The linear component of deformation for all arcs connecting two PSs are then estimated using periodogram approach. The estimated linear deformation component for all PS pixels is subtracted modulo-2pi from observed wrapped phase. The residual phases can be correctly unwrapped using the Nyquist sampling criterion since the most significant contribution of the deformation signal is assumed to be linear which is mostly the case. We applied the proposed approach to the ENVISAT ASAR images of southwestern Tehran basin. The estimated deformation rate is finally compared with deformation rate extracted from images of additional adjacent track using SBAS algorithm and the value of 22.9 mm/year as standard deviation of deformation rate demonstrates the high performance of the presented approach.

Acoustic remote sensing is a commonly used method for seafloor and riverbed sediment classification. In comparison with the conventional method of grab sampling, this method not only is of limited cost but also provides a complete overview of the bottom composition for the entire surveyed area. The use of single- and multi-beam echo-sounders data as an efficient way for seafloor and riverbed sediment classification is studied. The intensity and the shape of the received signals can provide useful information, which indicate the high potential capability of this limited-cost method. Because the received signals are subject to high statistical noise, a few mathematical and statistical tools are to be used to properly encounter this issue. The method of least-squares subject to non-negative and bounded constraints can be used for classification of multi-beam echo-sounder (MBES) data, while the principal component analysis is useful for single-beam echo-sounder (SBES) data. Two data sets on SBES and MBES will be used to illustrate the high potential capability of the proposed method for seafloor classification. The opportunities and challenges of these methods will be discussed.

Over-exploitation of groundwater has caused land subsidence in large rural areas located in Mashhad subbasin, northeast of Iran. Time series analysis using Interferometric SAR (InSAR) data has shown its ability to monitor the temporal evolution of land subsidence. In this paper, time series analysis based on Small Baseline Subset (SBAS) algorithm is applied to study the Mashhad sub-basin subsidence. 18 interferograms were generated using 12 ENVISAT ASAR images spanning between 2003 and 2005. In order to decrease the temporal decorrelation effect caused by the agricultural fields, only interferograms with small temporal baselines are used in the time series analysis. However, to prevent the solution from the rank deficiency, it is tried to generate as many interferograms as possible. Because the interferograms with large spatial baselines are influenced by the topographic artifacts, they are refined before using in the time series analysis. Moreover, the atmospheric-error free deformation corresponding to every acquisition time is retrieved by applying the smoothing constraint into the least squares solution. The maximum deformation rate in the study area is estimated as ~23 cm/yr. The compressibility of the aquifer system is finally investigated by the quantitative integration of the InSAR displacement measurements with observations of the hydraulic head fluctuations at few piezometric wells.

Today, aerial laser scanners (LiDAR) have an important role in 3D data acquisition from features. Bare earth information is very important in deferent applications such as DTM extraction, determination of traversable area, etc. Up to now, a lot of algorithms have been developed to automated filtering of LiDAR data. The weakness of most of these algorithms is inability to remove the large buildings. The main aim of this paper is to solve of this problem. Mathematical morphological operators were used for this purpose. First, the LiDAR data was filtered using one of the most efficient filtering algorithms (slope based filtering algorithm). Afterward, the result of filtering stage was improved by perform the proposed algorithm that is based on mathematical morphological operators. The result of accuracy assessment indicate a negligible increase in type I error and significant decrease in type II and total errors. Since, in filtering process, the type II and total errors are more important than type I error, performing this supplementary processing present very good result. Quantitative evaluation shows the output of the improved slope based algorithm with 20o slope threshold present the best result. In this case type I error increased from 4.98% to 5.27%, type II error reduced from 9.043% to 4.44% and total error decreased from 7.03% to 4.85%. Qualitative evaluation indicates the good performance of the proposed algorithm in removing the large buildings which are remained from filtering stage. Slope based filtering algorithm and Mathematical morphological operators were implemented in MATLAB software.