JOURNAL OF GEOMATICS SCIENCE AND TECHNOLOGY
Year:2014 | Volume:4 | Issue:2|Papers Count:20

Recently, hyperspectral images analysis has obtained successful results from information extraction in urban areas. Building detection is one of the important applications in processing hyperspectral images. In order to detect complete and precise building information from hyperspectral data, advanced data analysis methods are required. Algorithms based on spectral-identification are sensitive to spectral variability and noise in acquisition. In most cases, the spectral signature is unknown, so each pixel is separately examined and if it significantly differs from the background, it is regarded as an object. On the other hand, there are many algorithms e.g. Spectral Angle Measure (SAS), Spectral Correlation Similarity (SCS), Spectral Information Divergence (SID), Jeffries-Matusita Distance (JMD), Constrained Energy Minimizing (CEM) and Covariance-based Matched Filter Measure (CMFM) for building detection. In this study, first we employed the SAS, SCS, SID, JMD, CEM and CMFM algorithms for building detection. Then, in the next step to improve the spectral detection algorithms, two strategies, the combining algorithms using Adaptive Neuro-Fuzzy Inference System (ANFIS) method and spectral-spatial detection, was employed. Our experiments results demonstrate a significant improvement of accuracy using proposed strategies on two CASI hyperspectral images taken from an urban area.

SAR Image matching is a critical step in the radargrammetry, interferometry, DSM Generation and change detection applications of SAR image data. Image matching procedure in these images is much more difficult than the optical images due to high speckle noise and geometric distortions defining by layover, shadow, and foreshortening. Therefore, study on an efficient SAR image matching could be useful. In this paper, a multi-step strategy for this issue is proposed. These steps include SAR image despeckling, point feature extraction, feature allocation, local image matching and global image matching. In the proposed multi-step method we used current algorithms in photogrammetry and computer vision. In each step, several algorithms are experimented and compared to specify the final algorithm for that step. In our experiments, we used a pair of TerraSAR-X single-look slant-range complex (SSC) images with short and long baselines that were acquired over the city of Jam, southern Iran, in spotlight mode. The result shows that the proposed method could match appropriate number of points in both images with high accuracy and reliability. For example it could match 211 points with 1.9 pixel accuracy for long base line image pair with size of 700×700 pixels and 1603 points with 1.22 pixel accuracy for short base line image pair with size of 400×400 pixels. Therefore, the proposed SAR multi-step image matching strategy is appropriate for coarse matching level.

Speckle noise in radar images reduces the information that can be got from these images. This paper proposed speckle reduction in SAR images by soft thresholding of curvelet coefficients with emphasis on the preserving of edges. First, multiplicative speckle noise was transformed into an additive one by taking the logarithm of the original speckled image. Then curvelet transform was taken of logarithmically transformed image and the curvelet coefficients were thresholded by soft thresholding function. The results have been compared then to the results obtained by other widely-used adaptive filters including Frost, Gamma, Kuan, soft thresholding wavelet based filters and hard thresholding of curvelet coefficients. The results showed that the soft thresholding curvelet based filter offers better results than the mentioned filters (Mean Square Error, Normalized Mean Square Error and Mean Absolute Error indices reduced 27%, 27% and 15% respectively, also Equivalent Number of Looks (ENL) increased to 27.34 and 3.61 for RADARSAT1 and ENVISAT respectively). All in all results indicated that soft thresholding of curvelet coefficients besides high reduction of speckle noise is also very powerful in keeping the edges.

Roads detection in urban areas is of greater importance and is constantly considered as a main issue of researches in remote sensing community. According to spectral and geometrical variety of road pixels as well as their spectral similarity with other features such as buildings, parking lots and sidewalks and sometimes discreteness of roads due to having vehicles and trees in their neighborhood makes it problematic to precisely identify urban roads through satellite images. On the other hand, LiDAR data, providing height information, make it possible to recognize roads from other spectrally similar elements. Therefore, it has been used in many researches to identify different features like roads along with satellite images. In this paper Quick Bird large scale satellite image and LiDAR data through an object oriented analyses have been used to extract various types of urban roads. Proposed method has designed and implemented a rule oriented strategy based on a masking strategy. Afterwards, a supplementary strategy based on conceptual features design was used. The overall precision of class identification is 89.2 % and kappa coefficient is 0.832 which show a satisfactory precision according to different conditions and many interclass noises. Final results demonstrate high capability of object oriented methods in simultaneous identification of wide variety of road elements in complex urban areas using both high resolution satellite image and LiDAR data.

Nowadays, automatic processing and object extraction from data acquired by airborne sensors has been an important research topic in photogrammetric institutes. Airborne laser scanning system, which is commonly referred to as LiDAR, is a superior technology for three-dimensional spatial data acquisition from Earth’s surface in high speed and density. 3D city modeling is one of the main applications of LiDAR system. An important first step to reconstruct the building as one of the most important components of urban models is to identify and separate the building points from other points such as; terrain, trees, and vegetation. In addition, building roof segmentation is another important step in point cloud processing. In this paper, a multi-agent strategy is proposed for simultaneous extraction of building and segmentation of roof points from LiDAR point cloud. First, the ground and vegetation candidate points are removed from building points using local minimums of heights and returned pulse. Then different segments are extracted by analysis of the triangles formed on the remaining points by means of region growing method based on normal vectors. Finally, building segments are separated from other segments using area criterion and the united building segments are detected using a new method named ‘Grid Dilation’. The proposed method has been tested on the LiDAR data of the Vaihingen city, Germany. In addition to a visual interpretation, a quantitative assessment has been done. Due to lack of a ground truth, control points was selected manually from LiDAR point cloud and compared with points that classified using proposed method. The proposed method extracts the roof of buildings with an accuracy of 93%. Overall, the results indicate that proposed method could successfully identify the building segments without using additional resources, such as map or aerial photo. The main advantage of this method is its capability for extraction and segmentation of buildings containing parallel multi-level roof structures even with a very small height differences (e.g. 10 cm).

In studies of crustal deformation, volcanic models give valuable insights of volcanoes features and their behavior over time. Displacement field modeling, using analytical models needs to determine the parameters of the geophysical and geological volcanic reservoir. Therefore, modelling of volcanic magma reservoir was performed by an inverse problem resolution using the displacement field obtained from geodetic observations as the boundary values of the analytical models of deformation. This modeling was performed by genetic evolutionary optimization algorithm for Damavand volcano. Also, Displacement field during the years 2000 to 2001 for campi flegrei volcano was modelled. Comparing with the previous studies, the research has demonstrated favorable results. The result of displacement field modelling of the Damavand volcano has shown unexpected residuals (according to the magnitude of the displacement field) in some areas. This fact indicates presence of other displacement sources in addition to the volcanic deformation source in the region. The results lead to a reduction in the volume of the volcano magma reservoir with rate of equal to 0.001, located at a depth of 5.6 km below the ground. This volume reduction causes a small displacement in the area near the volcanic source. Modelled 2D coordinate of center of the source in the Lambert projection (4831.999E, 31328.536N) km and RMSE of inversion results 2mm was achieved.

Today, due to the limited natural resources of land, rapid population growth, rapid expansion of cities, future land use prediction is very important for land managers, planners and environmental specialists because land use change effect on ecosystem and also threaten vital resources . Modeling and analysis of the phenomenon of urban development provide comprehensive information to urban planners and managers to have better and more scientific planning. The main objective of this research is modeling urban growth for the city of Sanandaj, in the west of Iran using satellite imagery, Geographic Information Systems and logistic regression. The parameters are used in this study, including distance to developed area, distance to main roads, distance to green spaces, elevation, slope, distance to fault, distance to district centers and number of urban cell in a 3 by 3 neighborhood. Figure of Merit, Kappa coefficient and Percent Correct Match (PCM) have been used to evaluate goodness of fit of proposed model.

Motor vehicle crashes is one of the main problems of road transportation network in Iran. Exploring the significant variables related to concentration of motor vehicle crashes is vitally important in reducing crashes on a highway corridor. This study integrates spatial analysis with a K-Mean-based Hierarchical Agglomerative Clustering method to identify the correlation between major crash types at the concentration points of crashes and explore the most spatial factors that may lead to crashes. An experiment is designed and conducted on Qazvin-Rasht highway corridor using real crash records and spatial factors related to roadway geometry and its proximity features. Results showed that clustering crash records using the proposed method is 6.7 percent better than hierarchical agglomerative clustering and approximately 10 percent better than k-mean clustering method. Moreover, analyzing the concentration points of crashes using spatial functions and discovery of patterns and rules data mining approach explored type and specification of each cluster's crashes and revealed the impact of road geometry, traffic, urban development, activities and land uses in the proximity of highway corridors on increasing the rate and severity of motor vehicle crashes.

During last two decades Precise Point Positioning has been considered as one of the most important methods in satellite geodesy. Despite the efforts to improve the PPP precision, this method has not yet achieved the precision of Relative positioning methods. Most of the efforts on PPP improvement focus on the processing models and phase ambiguity. Modeling the tropospheric delay is very crucial to achieve high precision in Precise Point Positioning. There are different methods to estimate this delay such as ray-tracing or using appropriate mapping functions e.g. GMF, VMF, …, which relate the zenith path delay to slant path delay. In this paper, the ray-tracing slant path delay has been used as a reference value. Then three other delays are calculated using zenith path delay obtained from PPP and Global mapping function in different ways. The difference between the delay computed by ray-tracing method and those three other delays is applied to the RINEX observation files. The new RINEX files are implemented for PPP reprocessing. Comparing the achieved results, with the ITRF coordinates of points, shows that applying the difference of slant path delay from ray-tracing and slant path delay which is computed by zenith path delay of PPP (using 88% of hydrostatic global mapping function and 12% of non-hydrostatic global mapping function) to the RINEX files, improve positioning accuracy.

During the recent years, GPS has been introduced as a unique tool to study the propagation of electromagnetic waves through the atmospheric layers, in particular the ionosphere. The ionosphere is currently the major source of errors in GPS positioning which its effect on wave propagation is dependent on the amount of TEC and signal frequency. In this paper, the temporal-spatial variations in Iranian TEC has been studied using observations of 40 IPGN stations. For this purpose, GPS observation data, consisting 10 days in 2012 and 3 days in 2013 which are the first days of solar months, have been processed via Bernese 5.0 GPS software (first days of each season is included in our data). To do so, regional ionosphere modeling, estimation of TEC values and calculation of the receiver DCB have been performed by expansion of spherical harmonic functions up to the sixth degree and order. Furthermore, the difference between smoothed code observations from the first and second frequencies has been used to eliminate geometric effects, such as satellite and receiver clock biases and tropospheric delays. Blunder and cycle slip detection as well as code observation smoothing have been performed during preprocessing steps. The results have shown that the maximum and minimum ionosphere effects are related to the spring and winter seasons, respectively, and the maximum TEC value occures at about 14:00 local time. Moreover, we can see significant TEC variations in accordance with changes in latitude. on the other hand, the maximum and minimum TEC values in Iran is associated with the minimum and maximum latitudes, respectively. In the spring of 2012, 100 TECU was observed as the TEC value. If the satellites are near the horizon and by using second frequency, It could lead to an error 75 meters on the satellite–receiver range.

This study aims at finding Moho Depth in the Zagros zone and estimating crustal thickness through Euler Deconvolution method. Euler Deconvolution method is an automatic one to estimate depth, shape and place of magnetic and gravity sources which is based on using field derivatives in Euler’s homogenous equation. In using Euler Deconvolution method it is important to identify structural index and window size of estimator, these parameters strongly affect the final results. The study first uses spherical harmonic coefficients of EIGEN-GL04C geopotential model to calculate free air gravity anomaly in j=29.25o - 34.75o & l= 48.25o - 53.75o region, then Moho Depth was estimated using free air gravity anomaly and Euler Deconvolution method for various structural indices and window sizes. The best structural index and window size were resulted from comparing Moho depth of Euler Deconvolution and of receiver function method (based on seismic studies) in 14 seismic stations of the region and finally the results were compared to CRUST 2.0 model. Results declare that for 0.5 structural index and 40-45 km window size the best Moho depth is estimated for the region.

The main topic of this research is to investigate different gravimetric reduction in the context of precise geoid determination. Gravimetric reduction perform an essential role on precise geoid determination, particularly in rugged areas. A numerical investigation was performed in the rugged area of the Northwest Iran within the geographical boundaries 35.5>j>39.5 and 44.5>l>49.5 to study gravimetric geoid solutions based on the Rudzki inversion scheme, Helmert’s second method of condensation, RTM, and the topographic-isostatic reduction methods of Airy-Heiskanen (AH) and Pratt-Hayford (PH). The results shows Rudzki geoid performs as well as the Helmert and RTM geoids (in terms of standard deviation and range of minimum and maximum values) when comparing to comparison with the GPS-levelling geoid of the test area. Rudzki inversion the sole gravimetric reduction scheme which doesn't change the equipotential surface and thus doesn't need the calculation of the indirect effect.

Attitude determination of a moving platform, i.e. roll, pitch, and heading, is one of the essential issues in hydrographic surveying. Commonly, the attitude parameters are measured by motion reference units (MRUs) that are installed onboard the platform. Indeed, in order for bathymetry, the accuracy of the onboard MRU must be in agreement with the corresponding IHO standards. As an alternative method, on the other hand, the platform attitudes can reliably be determined via GPS phase observations. Therefore, GPS-based methods for determining the attitudes can be regarded as a suitable experimental tool for quality control of onboard MRUs. In this contribution, a new GPS-based method is presented for quality assessment of MRU. The proposed method can algorithmically be explained as follows: (i) Establishment of a GPS antenna at berth near to the field operation area. (ii) Installation of four GPS antennae onboard the vessel. (iii) Realization of the body frame (b-frame) with the origin at one of the onboard antennae, hereafter Ant. 1, and measurements of the b-frame coordinates of the other onboard antennae. (iv) Starting GPS observations at about 20 minutes before departure for ambiguity resolution, while the vessel is moored at the berth. (v) Sailing from minimum to maximum cruise speed while GPS and MRU observations are going on. (vi). Realization of a local level frame (ℓℓ-frame) at Ant. 1 as well as transformation of the GPS geocentric coordinates to the established ℓℓ-frame. (vii) Using GPS observations to compute the attitude parameters and the corresponding precisions based on the least squares. (viii) Comparing the GPS-derived attitude parameters with those derived by the onboard MRU. (ix) Estimation of the accuracy and bias of the MRU. As the case study, the presented method is applied to 27-meter long survey vessel of National Geographic Organization (NGO) based on four dual-frequency Javad GNSS receivers with choke-ring antennae in the surrounding waters of Kish harbor at different cruise speeds ranging from 5 to 12 knots. Numerical results asserted achievement of mean precisions of 28", 28" and 17" for the pitch, roll and heading, respectively, using carrier phase observations according to the proposed procedure. Moreover, the estimated accuracies for the pitch, roll and heading of the onboard MRU are 7.5', 7.7' and 32.6', respectively. Furthermore, the correction coefficients were computed for the pitch, roll and heading of the onboard MRU. The overall results approved the efficiency of the proposed method to reliably derive the attitudes of moving platforms.

One of the geoid determination methods is solving earth gravity boundary value problems based on the first derivative of the ellipsoidal Poisson’s integral, for gravity values transformation from the Earth’s surface to the gravity potential on the reference ellipsoid. This method is identified by transforming differential gravity observation on the earth’s surface through using a reference gravity field, to the gravity potential on the reference ellipsoid. Reference gravity fields are valid to use in non-topography areas, but applying them in the inner zone, where we have topography mass, would be associated with some error. In this paper, mathematical formula for estimating this error and the necessity of applying that in restore step of one-step inversion method has been evaluated. For the accuracy assessments, an area with real gravity data in the west of Iran has been considered. The results confirmed the accuracy of this correction term in computing reference field effect on potential space in restore step of one-step inversion method.

Accurate quantitative estimation of vegetation biochemical and biophysical characteristics is necessary for a large variety of agricultural, ecological and meteorological applications. Among agricultural products in Iran, strategic and economical importance of Pistachio highlights the essential planning for improvement and increase of its production. The aim of this study is to compare the utility of statistical multivariate calibration techniques, including Stepwise Multiple Linear Regression (SMLR) and Partial Least Squares Regression (PLSR) with univariate techniques such as narrow band vegetation indices using hyperspectral data for estimating chlorophyll content of Pistachio trees. Pistachio leaves were collected in different growth stage. Spectral and chemical measurements were obtained from the collected samples at the laboratory, using ASD Field Spectrometer III, SPAD measurements and wet chemical analysis. Narrow band indices derived from all possible two-band combinations of reflectance and first derivative data were assessed and the best band combinations with the highest R2 values were selected and used in linear regressions to predict the studied parameters. Among studied indices, the narrow band RVI index with wavelengths 670 nm and 734 nm using first derivative were recognized as the best index for predicting chlorophyll (R2cv=0.72, RRMSEcv=0.25). The results of multivariate analysis showed that PLSR and SMLR techniques using first derivative data are better than narrow band indices in chlorophyll prediction, respectively (R2cv=0.79 and RRMSEcv=0.21). In a nutshell this study showed that multivariate calibration techniques increase the accuracy of predicting chlorophyll content in Pistachio leaves comparing to univariate techniques. Also first derivative transformation would increase the accuracy of predicted parameters compared to reflectance values. The results highlight the benefits of using hyperspectral measurements in assessing the biochemical parameters of Pistachios trees and therefore are recommended for analysis of health and growth status of agricultural products.

Urban planners and GIS users need to update their land use information in order to design and perform urban planning. Therefore the maps need to be updated regularly. An appropriate approach of updating them is using aerial images. Due to the high resolution of these images, their low cost, and also the possibility of aerial image acquisition in Iran, using aerial images for map updating has been a great concern. In order to update the available maps, the object changes should be understood, and then reconstructed. In this case and according to the properties of large scale maps (1.2000), buildings are very important features to be updated. In this research, a novel method based on Snake’s theory is proposed to detect buildings’ outlines. Also, a different Active Contour Model was used in order to compare the results of the proposed method. DSM and Ortho Images were produced using Stereo images and enough control points, the curve needed to initialize the snake model was obtained from the generated DSM. According to the results, the proposed method in this research provides the completeness of 86.043%, and the correctness of almost 91.124%.

Building detection from areal and satellite images is an active discussion in remote sensing and machine vision in recent years. Urban areas usually are dense and consist of complex components such as compact tree areas and buildings with gable roof and glassy parts. Classification algorithms which are applied to these kinds of data sets will be faced many problems. In this paper to deal with the aforementioned problems, the object based features; height and etc. have been investigated for classification by the use of support vector machine in the object based and pixel based analysis. It should be noted that pixel based analysis performed in two different states with features which are extracted from aerial imagery and LiDAR data. The proposed method consists of three general steps; the first step is data preparation and features extraction. The second step is classification by the use of support vector machine in object based and pixel based analysis; In the third step, post processing is applied then results of classifications are compared and evaluated with ground truth data. In this study the final goal is to achieve optimized algorithm using various features. with comparison of Kappa coefficient in three classifications; 0.97 in object based analysis, 0.88 in first state of pixel based analysis and 0.97 in second state of pixel based analysis, it is obvious object based analysis achieved the best result due to using features such as shape and structure. More over using LIDAR data in second state of pixel based analysis increased the accuracy of pixel based classification.

In recent years urban classification becomes very important caused by urban growth and high rate of urbanization. Classification and recognition of urban classes in different information layers for supplementation and updating of urban database is considered by researchers and managers. The goal of this paper is comparison and evaluation of different urban classification methods base on object based analysis by using LIDAR data and optical imagery. This paper includes three main phases. First step of workflow is co registration and preprocessing of LIDAR data and high resolution imagery to prepare multi source data for urban classification. Second step followed by hierarchal multi resolution segmentation at different scales to exhibit different features which are consist of building, roads, vegetation area and vehicles. Segmentation contains three main levels. Selection of hierarchal segmentation parameters is a try and error task and segmentation validation is done by visual assessment. After object production convenient features should be introduced to the classification algorithms. Finally thresholding, nearest neighbor and fuzzy nearest neighbor classification at each level of hierarchy was performed. Last step is result assessment and interpretation. By result evaluation, nearest neighbor classification with 0.99 over all accuracy was nominated as best classifier in first level. In second level of hierarchy nearest neighbor classification with 0.985 shows the highest overall accuracy. In third level fuzzy nearest neighbor classification and thresholding show 0.841 over all accuracy.

GIS can effectively work in the field of site selection urban waste landfill through gathering, weighting, analyzing and displaying spatial data. Site selection for urban waste landfill in very important and its process is complicated and has various quality and quantity criteria. In this paper a hybrid method has been prepared to determine suitable site for urban waste landfill. For doing this weights of requirement layers are specified by AHP method than noted locations are categorized in three classes: Best, Good and Weak. In the last phase, first zones in the best class are ranked by Promethee V method and then with regarding practical constraints such as budget restriction and their internal connections, the best region(s) are determined for urban waste landfill though solving a Zero-One programming optimization procedure. Also the given method has been executed on Tabriz city as a case study and doing sensitive analysis on weights has shown that the selected alternative is suitable and has enough robustness.

The aim of this study is firstly taking the advantages of effects of various change detection techniques. These techniques are differentiated by the way they use the dataset which could lead us to gain complementary outputs and ultimately achieve higher accuracy in change detection and secondly comparison of performance of three various group of decision level fusion schemes. The study area is the city of Karaj in Iran. Satellite images used in this study are ASTER images captured on July, 2001 and September, 2012. Change detection have been performed with fuzzy post classification and combined fuzzy spectral–temporal analysis techniques, then results of this techniques fused by means of three various group of decision level fusion schemes included:1) Averaging operators 2) Maximum operator 3) fuzzy integral operators. Results of accuracy assessment that has been done by available land cover maps firstly has shown improvement of change detection accuracy over each single fuzzy change detector and secondly has demonstrated advantage of fuzzy integral methods with respect to two other methods.