kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
A New Structural Matching Method Based on Linear Features for High Resolution Satellite Images
1
15
FA
Somayeh
Yavari
K. N. Toosi University of Technology
Mohammad Javad
Valadan Zoej
K. N. Toosi University of Technology
Mahmod Reza
Sahebi
K. N. Toosi University of Technology
Mehdi
Mokhtarzade
K. N. Toosi University of Technology
Along with commercial accessibility of high resolution satellite images in recent decades, the issue of extracting accurate 3D spatial information in many fields became the centre of attention and applications related to photogrammetry and remote sensing has increased. To extract such information, the images should be geo-referenced. The procedure of georeferencing is done in four main steps of extracting some control information such as point, line or areal features, matching, transformation function estimation and finally resampling. Among different control features, the lines are more considered due to their unique characteristics such as easier procedure of automatically extracting and matching as well as abundance of linear features in satellite images especially in urban areas. To reach an automatic georeferencing procedure, it is inevitable to automate the matching step as one of the most challenging process especially in heterogonous spaces such as image and map spaces. So, in this paper, the automatic matching procedure is performed and assessed using a new structural linear feature-based matching method with no need to any initial information. This method is based on using the most inherent conceivable information of the extracted features. The purpose of this paper is to find all possible match-lines through finding the correspondence of two specific patterns. The proposed method is done in three main phases of high quality pattern selection, matching as well as the final-phase. Additionally, new concepts of mathematically-generated-lines which are produced by extension and intersection of line-segments in two spaces as well as mathematically-generated-points which are the key-points of that lines are introduced and used to find the match-lines. In this paper, the impact of different numbers of stopping-criteria as well as one of the thresholds is studied experimentally. The results show the high potential of the proposed method to find more than 80 percentages of match-lines with 100 percentages of accuracy and reliability in a low computational time.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Wheat Leaf Rust Disease Severity Estimation Using Reflectance Spectrum Coding Methods
17
30
FA
Mohamad Reza
Mobasheri
Khavaran Institute of Higher Education
Pegah
Darouei
K. N. Toosi University of Technology
Davood
Ashourloo
Shahid Beheshty University
Using spectroradiometry and remote sensing techniques is an effective and rapid method in diagnosing vegetation diseases which enforced mostly by using spectral vegetation indices and statistical methods. The present study aimed to deploy encoding technique for the reflectance spectrum of the wheat leaves to assess the severity of the Rust disease. This is unlike to the spectral vegetation indices in which the shape of the spectrum, in all bands, independent from time and place is examined. A comprehensive laboratory spectroradiometry were used in the present study in which different stages of the development of the wheat rust stage were considered. The encoding methods were applied to the reflectance spectrum and its derivatives by the Equal Intervals Coding (EIC) and 1bit, 2bit and 3bit information and Threshold Coding (TC) methods for the 500-800 and 400-1050nm wavelength ranges. In this respect, the healthy green leaf code used as a reference. Then the similarity between any other leaf codes and the green leaf code were used to find the degree of the severity of the disease. Beside the reflectance spectrum, the progress of the disease on the leaf under observation were determined using a digital camera. The best result found to be for 3bit- TC in the 500-800 nm wavelength region with R2 and RMSE of the order of 0.95 and 0.05, respectively. Finally, the portion of the rust affected leaf was determined in four levels based on the green spot absence in which, the overall accuracy and Kappa coefficient were 85.96% and 0.81%, respectively.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Exploring the Impact of Topographical and Climate Factors on Generation of the Vulnerability-map of Leptospirosis
31
50
FA
Mehrdad
Ahangarcani
K. N. Toosi University of Technology
Mahdi
Farnaghi
K. N. Toosi University of Technology
Mohammad Reza
Shirzadi
Center for Disease Control (CDC), Ministry of Health and Medical Education
Leptospirosis is one of the most widespread zoonotic disease caused by Leptospira bacteria. It is found wherever human is in direct or indirect contact with Leptospira bacteria thorough infected animals as well as contaminated soil or water. The disease is mostly found in tropical, subtropical, hot, and humid areas. The main objectives of this study are to investigate the seasonality relations between the topographical and climate factors, including altitude, slope, vegetation, average temperature, average humidity, precipitation and number of freezing days and incidence of Leptospirosis as well as modelling of Leptospirosis using support vector machine at the district level in Northern provinces of Iran. Pearson’s correlation analysis was conducted to examine the type and strength of relationships between the topographical and climate variables and Leptospirosis incidence. Results of Pearson’s correlation analysis indicate that average humidity, average temperature and rainfall were the most influential environmental factors which as effect on prevalence of Leptospirosis in the study area. Statistical analysis showed that most cases of the Leptospirosis prevalence have been recorded in the late spring and summer. On the other hand, the lowest incidences have occurred in winter. Also, high distribution of leptospirosis mainly located in the central areas of Guilan province, the eastern parts of Mazandaran province and western regions of Golestan province with a mild and humid climate and abundant rainfall. Eventually, performance of support vector machine (SVM) model evaluated by area under the ROC curve. The output maps showed that SVM model has excellent performance in the vulnerability mapping of Leptospirosis.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Geometrical Deformation Analysis of Gotvand-Olya Dam Using Permanent Geodetic Monitoring Network Observations
51
72
FA
Monavar
Ebrahimipour
K. N. Toosi University of Technology
Yazdan
Amerian
K. N. Toosi University of Technology
Mehdi
Raoofian Naeeni
K. N. Toosi University of Technology
In this paper, two-dimensional deformation analysis of the Gotvand-Olya dam is done using daily, monthly, seasonal and annual displacement vectors derived from permanent observations of the dam geodetic monitoring network. The strain tensor and its invariant parameters like dilatation and maximum shear are computed as well. Nonlinear finite element interpolation based on C1 Cubic Bezier interpolant function is used to compute the strain tensor in the Lagrangian approach. According to the results, the dilatations computed from the daily observations on March 25, 2014 and 2015 reveal the extension of dam, while on March 25, 2016 they indicate contraction in dam’s body and crown. Monthly results show the extension of dam’s geodetic network in September 2016 and contraction in September 2014 and 2015. Seasonal results showed the contraction in summer 2014 and extension in winter 2014 in dam’s geodetic network. Dilatation values from annual displacement vectors indicate contraction in dam from July 2014 to July 2015 and extension from July 2015 to July 2016. Statistical test is used to investigate the statistical significance of daily, monthly and seasonal strain tensor invariants.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Presenting a Morphological Based Approach for Filtering The Point Cloud to Extract the Digital Terrain Model
73
96
FA
Parham
Pahlavani
University of Tehran
Hamid Reza
Sahraiian
University of Tehran
Behnaz
Bigdeli
University of Shahrood
The Digital terrain model is an important geospatial product used as the basis of many practical projects related to geospatial information. Nowadays, a dense point cloud can be generated using the LiDAR data. Actually, the acquired point cloud of the LiDAR, presents a digital surface model that contains ground and non-ground objects. The purpose of this paper is to present a new approach of extracting the digital terrain model from the digital surface model. In the first step, noises were removed by preprocessing; then the irregular point cloud was converted to raster data. In the next step, the proposed gradual geodesic dilation and labeling approaches scan were applied in order to detect and eliminate the non-ground objects. The basis of gradual geodesic dilation approach was to increase the structural element size in each step, investigate the height heterogeneity and remove the non-ground objects, gradually. Also, utilizing the innovative scan labeling approach which operated based on slope differential helped to remove the non-ground objects completely.
Finally, the non-ground objects were removed and the lost regions were retrieved and the digital terrain model was generated by interpolation. For analyzing the proposed approach, the reference data of the ISPRS was employed. The analyzing results in the five test areas indicated 4.61%, 6.97% and 3.17% for Type I, Type II and total errors, respectively. These results clarify the good performance of the proposed approach for detecting the non-ground objects.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Land Cover Subpixel Change Detection using Hyperspectral Images Based on Spectral Unmixing and Post-processing
97
117
FA
Mahdi
Hasanlou
University of Tehran
Seyed Teimoor
Seydi
University of Tehran
The earth is continually being influenced by some actions such as flood, tornado and human artificial activities. This process causes the changes in land cover type. Thus, for optimal management of the use of resources, it is necessary to be aware of these changes. Today’s remote sensing plays key role in geology and environmental monitoring by its high resolution, wide covering and low cost of data receiving from the earth and it has many applications such as change detection. To manage the resources optimally, in local and gloal scale, accuracy and being on-time are very substantial. Hyperspectral images, with thier high ability of spectral resolution, can improve change detection in result and extract more detail of changes. In this research a new method of change detection for hyperspectral imagery using the Image-Differencing, Otsu and spectral unmixing algorithms is presented . The proposed method is presented in three steps: (1) Data correction using image differencing method and data conversion to new computing space. At this space, the changed areas would be more outstanding compare to previous space. (2) the decision about the nature of endmembers is made using Otsu algorithm. (3) spatial resolution enhancement based on abundance map. The proposed method can automatically extract binary change map. In addition, this method provides information about the nature of change in sub-pixel level. To examine the performance of the proposed method, the hyperspectral imagery (by Hyperion sensors) from Chiangsu fields in china and a simulated data from the AVIRIS sensor were used. The results show the high accuracy of the proposed method in comparison with other methods. Its overall accuracy is more than 93% and its kappa coefficiency is 0.85 and mean false alarm rates is under 7% for China dataset. And also, the results for second dataset are as follow: the overall accuracy is more than 99% and kappa coefficiency is 0.82 and mean false alarm rates is under 1%.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Fusion of LST products of ASTER and MODIS Sensors Using STDFA Model
119
132
FA
Alireza
Bazrgar Bajestani
University of Tehran
Mahdi
Akhoondzadeh hanzaei
University of Tehran
Land Surface Temperature (LST) is one of the most important physical and climatological crucial yet variable parameter in environmental phenomena studies such as, soil moisture conditions, urban heat island, vegetation health, fire risk for forest areas and heats effects on human’s health. These studies need to land surface temperature with high spatial and temporal resolution. Remote sensing satellite sensors due to their technical constraints cannot take the high spatial and temporal land surface temperature data simultaneously. For example combining Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) LST products have spatial resolution of 90 m with repeat cycle of 16 days, whereas Moderate Resolution Imaging Spectro-radiometer (MODIS) LST products have spatial resolution of 1 km with daily repeat cycle. To address this shortage, this work used the Spatial and Temporal Data Fusion Approach (STDFA) to estimate the high spatial and temporal resolution LST by ASTER LST and MODIS LST products. This method was tested and validated in study areas located in Tehran, Iran. The MODIS daily 1-km LST product and the 16-day repeat cycle ASTER 90-m LST product are used to produce a synthetic “daily” LST product at ASTER spatial resolution. The actual ASTER LST products were used to evaluate the precision of the synthetic daily LST product. Here, the correlation coefficient was equal to 0.88, Root Mean Square Error (RMSE) reached about 3.38 K. The results showed that the algorithm can produce high-resolution temporal synthetic ASTER data that were similar to the actual observations.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Design and Implementation of an Intelligent Photogrammetric System for Control and Guidance of Reconstructive Surgery
133
147
FA
Babak
Ghassemi
K. N. Toosi University of Technology
Hamid
Ebadi
K. N. Toosi University of Technology
Farshid
Farnood Ahmadi
University of Tabriz
The digital image contains efficient and useful information which enables measurement and data acquisition. One of the methods that facilitate measuring and interpreting objects, using the image solely, is close-range photogrammetry. Among the various fields of science, whenever a precise measurement is required, this approach can be applied. One of these fields is Medical Sciences that due to high speed and accuracy of close-range photogrammetry, it can create three-dimensional models. To generate the model there is no need for direct contact with the patients consequently there are no side effects. So in this respect, it is better than other conventional methods in medical imaging which are often invasive procedures. This branch of photogrammetry is known as medical photogrammetry.
In this paper, application of intelligent close range photogrammetry in control and guide of reconstructive surgeries is presented. The proposed method is evaluated in a case study of the human face using facial recognition algorithms in two-dimensional space and matching in three-dimensional space. The main objective of this research is to design a system by integrating close-range photogrammetry and intelligent algorithms to guide and control reconstructive surgeries using two-dimensional images and three-dimensional models. The output is geometrical parameters and the changes of input face to transform to the proposed face that surgery is done on part in question at the discretion of the physician. The three-dimensional point cloud from face model produced with 143-micron accuracy and point clouds of similar faces were registered together in the range of 2mm to 3 mm in rmse.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Speckle Reduction in Synthetic Aperture Radar Images in Wavelet Domain Using Laplace Distribution
149
162
FA
Ramin
Farhadiani
University of Tehran
Abdolreza
Safari
University of Tehran
Saeid
Homayouni
University of Ottawa
Speckle is a granular noise-like phenomenon which appears in Synthetic Aperture Radar (SAR) images due to coherent properties of SAR systems. The presence of speckle complicates both human and automatic analysis of SAR images. As a result, speckle reduction is an important preprocessing step for many SAR remote sensing applications. Speckle reduction can be made through multi-looking during the image formation or using spatial filters as a preprocessing step. However, these methods have some limitations such as a decrease in spatial resolution or smoothening of details and edges. To overcome these problems, Multi-Resolution Analysis (MRA), such as wavelet transform, should be used. In this paper, a despeckling method based on the Bayesian theory and Maximum a Posteriori (MAP) estimator in the wavelet domain was proposed. The noise-free wavelet coefficients of the logarithmically transformed image and the noise in the wavelet domain were modeled based on the Laplace and Gaussian distributions respectively. VisuShrink, SureShrink, and BayesShrink methods were also implemented and applied to both simulated and real SAR data for comparison purpose and to assess the proposed method. PSNR and beta edge preserving index were used to evaluate the performance of simulated SAR data, while ENL was employed to evaluate the real SAR data. Experimental results of despeckling showed the superior performance of the proposed method in suppressing the speckle efficiently and preserving better the spatial details in the SAR image.
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
6
4
2019
3
1
Urban Land-Use Allocation By A Cell-based Multi-Objective Optimization Algorithm
163
186
FA
Jamshid
Maleki
University of Tehran
Farshad
Hakimpour
University of Tehran
Zohreh
Masoumi
University in Advanced Studies in Basic Sciences
Allocating urban land-uses to land-units with regard to different criteria and constraints is considered as a spatial multi-objective problem. Generating various urban land-use layouts with respect to defined objectives for urban land-use allocation can support urban planners in confirming appropriate layouts. Hence, in this research, a multi-objective optimization algorithm based on grid is proposed to generate well-distributed solutions in objective space. In order to preserve diversity in Pareto front approximation, a grid is defined in objective space. The cells of this grid cluster the solutions and determine the suitable solutions for next generation in optimization process. The land-uses of region 1 of the district 7 of Tehran is used to assess the efficiency of the algorithm in optimizing urban land-use allocation. The results of the proposed algorithm are compared with the results of Non-dominated Sorting Genetic Algorithm II and III (NSGA-II and NSGA-III). Comparing the results indicate that the proposed algorithm acts better than NSGA-II and NSGA-III in preserving diversity and improving the convergence of the solutions in Pareto front.