kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Automatic mode detection in transportation using GPS data from mobile devices and neuro –fuzzy system
1
20
FA
Elahe
khazaei
K.N.Toosi University of Technology
Ekhazaei@mail.kntu.ac.ir
Y
Ali Asghar
Alesheikh
K.N.Toosi University of Technology
alesheikh@kntu.ac.ir
N
mohammad
Karimi
K.N.Toosi University of Technology
mkarimi@kntu.ac.ir
N
10.29252/jgit.5.1.1
Cognition of travel mode and travel demand is of prime importance to transportation communities and agencies in every country. If the precise transportation modes of individual users are recognized, a more realistic travel demand can be considered. Also, in location-based service, the knowledge of a traveler’s transportation mode is applied to send targeted and customized informative advertisements. This study examines the feasibility of using a neuro-fuzzy inference system to automatically detect the mode of transportation from GPS data collected by GPS-enabled mobile phones. To achieve this, the knowledge was extracted in the form of fuzzy rules from the data and, then, the rules are being used for determination of transportation’s mode. For this purpose, the model was examined in two cases. In the first case, all GPS data from mobile devices were used, while in the second case the critical point algorithm was exercised. In addition to reducing the size of required GPS datasets, the critical point algorithm decreases data collection cost and saving mobile phone resources such as its battery life. The results showed that the suggested model have the capability of detecting a transportation mode with 94/1 percent accuracy in case of using all GPS data and 95.5 percent accuracy in case of using critical points.
Neuro-fuzzy system, Transportation Mode detection, Critical points, GPS data.
http://jgit.kntu.ac.ir/article-1-118-en.html
http://jgit.kntu.ac.ir/article-1-118-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Unsupervised Change Detection in Multitempolar SAR Images Based on Integration of Clustering and Active Contour Model
21
37
FA
armin
moghimi
K.N.Toosi University of Technology
moghimi.armin@gmail.com
Y
safa
Khazai
Imam Hussein Comprehensive University
N
hamid
ebadi
K.N.Toosi University of Technology
N
10.29252/jgit.5.1.21
In this study, a method for unsupervised change detection in multi-temporal SAR images has been presented based on integrating clustering and active contour model. In this method, texture information is extracted by using Gabor filter in different scales and directions. KPCA transformation is also applied to reduce the dependency between the extracted features and image information. Moreover, Discrete Wavelet Transformation (DWT) and Gustafson-Kessel clustering (GKC) methods are used respectively to generate the difference image and the initial contour for the active contour model. In the final step, the region-based non-parametric active contour model is used for producing the change image containing changed and unchanged regions. For performance evaluation of the proposed method, two sets of high resolution multi-temporal TerraSAR-X images are considered. Experimental results of unsupervised change detection method show that, the total error rate of the proposed approach for the first data set are decreased respectively to 4.95%, 3.30% and 3.34% compared to that of the Chan-Vese, MRF and EMMRF methods and for the second data set, the total error rate of the proposed method are decreased to 2.56%, 1.86% and 1.87 As well. Moreover, the results showed that the use of GKC method leads to production of the initial curve with minimal convergence time for the active contour model. Also, the use of active contour model improves the accuracy of change map creation using a repititive process.
Multi-temporal SAR images, Gabor filtering, Gustafson-Kessel clustering (GKC), Active contour model.
http://jgit.kntu.ac.ir/article-1-421-en.html
http://jgit.kntu.ac.ir/article-1-421-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Introduction of Spherical Slepian Spectral Combination and Its Investigation on Global and Regional Ionospheric Models
39
48
FA
hossein
etemadfard
K.N.Toosi University of Technology
hossein_etemadfard@yahoo.com
Y
Masoud
Mashhadi Hossainali
K.N.Toosi University of Technology
N
10.29252/jgit.5.1.39
Lack of interaction and ability to combine between ionosphere models are important problems among them. This study has investigated the interaction between global and regional ionospheric models which are according to the base mathematical functions. Here, spherical Slepian base functions have been suggested for spectral combination. They can be defined in global and regional scales. Two sets of coefficient were assumed for spherical Slepian base function. One set (direct model) is extracted from the direct observation of GPS stations. Another set (indirect model) is derived from spherical harmonic products of Global Ionosphere Models (GIMs). GIM’s efficiency has been modified on a sub-space which was equal to pervious set.
Maximum degree has considered equal to 15 for implementation of spherical Slepian spectral combination theory. Also, the Arctic region has been taken as study area where it is the spherical cap by latitude upper than 60 degrees. Observations of three GPS stations are used for evaluation of models. They have not contributed for direct and indirect modelling. Root Mean Square of Errors (RMSEs) for GIMs, direct, indirect and combining models are equal to 3.7, 2.2, 1.9, 1.4 TECU, respectively. In other words, results show that combining method has improved the ionospheric modelling here. The combining method can decrease the effects of the lack of stations in the indirect model and the inappropriate distribution of stations in the direct one. Therefore, the RMSE of the combining model is less than other models in the Arctic region.
Spectral Combination, Spherical Slepian, Ionosphere
http://jgit.kntu.ac.ir/article-1-423-en.html
http://jgit.kntu.ac.ir/article-1-423-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Agent-Based Modeling of Urban Growth With Communications
Inspired by Particle Swarm Optimization Algoritm
49
64
FA
Farnaz
Kaviari
K.N.Toosi University of Technology
Farnaz_Kaviari@ mail.kntu.ac.ir
Y
Mohamad Sadi
Mesgari
K.N.Toosi University of Technology
N
Farhad
Hosseinali
Shahid Rajaee Teacher Training University
N
Samane
Vaezi
K.N.Toosi University of Technology
N
10.29252/jgit.5.1.49
Population growth, urbanization and immigration from rural areas to cities results in increasing expansion of
the cities. The adequate urban development requires proper and accurate planning, such those facilities for the
new areas that are provided and environmental impacts are lessened. Computer based simulation and
prediction of urban growth can be assumed as a good start for urban planning. In this research, a new agent
base simulation of urban growth is developed, in which the communication and decision of agents are imitated
from the Particle Swarm Optimization (PSO) algorithm. The model is tested on the urban growth of Zanjan
city- Iran between 2005 and 2015. In this model, land developers are classified into three groups of agents
according to their income level. These agents search the environment and find proper lands for development
according to their priorities and conditions. The output of the model is 74% similar to the reality according to
the Kappa index. This and other results show that the model can predict the expansion of the city adequately.
Moreover, the comparison made shows that modeling of the relations and communications between agents
similar to PSO can slightly improve the quality of the model. The results showed the adequacy of the proposed
agent-based modelling for the simulation of urban growth. To have a more accurate model, it is recommended
to model the behavior of the agents with more details and also to consider the competition between agents.
Urban Growth, Simulation, agent, PSO
http://jgit.kntu.ac.ir/article-1-424-en.html
http://jgit.kntu.ac.ir/article-1-424-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Impact of GRACE inline and Bender gravity satellite missions on
the tidal components errors de-aliasing of N2 and S2
65
87
FA
forouzan
ghasser mobarakeh
University of Isfahan
fghasser@yahoo.com
Y
Siavash
Iran Pour
University of Stuttgart
N
Alireza
Amiri-Simkooei
University of Isfahan
N
Vahab
Nafisi
University of Isfahan
N
10.29252/jgit.5.1.65
Ocean tides cause noticeable aliasing errors in the gravity field from single pair space-borne graimetric
missions like GRACE. Several studies about future gravity missions have shown that constellations with two
or more GRACE-like tandems can lead to a significant reduction of aliasing error from all kinds of highfrequency
signal sources. Despite such reduction, tidal aliasing will remain an error source still. This study
investigates the efficiency of tidal error dealiasing in the post-processing mode for such future dualpair
missions. To that purpose, we analyze the way a certain satellite mission sampling each tidal constituent.
Given the repeat orbit patterns and the observation time span, we examine and model the aliasing periods and
amplitudes constitute by constitute. Results show that a double-pair formation has de-aliasing function
comparing to a single-pair formation in terms of distribution and amplitude of ocean tide aliasing error. With
least-square (LS) estimation, the aliasing error can be reduced significantly.
GRACE inline . ocean tide .de-aliasing.
http://jgit.kntu.ac.ir/article-1-425-en.html
http://jgit.kntu.ac.ir/article-1-425-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Evaluation of SLIC superpixel and DBSCAN clustering algorithms
in segmentation of ultra-high resolution remote sensing imagery
over urban areas
89
109
FA
Ahmad
Hadavand
University of Tehran
ahadavand@ut.ac.ir
Y
Mohamad
Saadatseresht
University of Tehran
N
Saeed
Homayouni
University of Ottawa
N
Zeinab
Gharib Bafghi
German Aerospace Center
N
10.29252/jgit.5.1.89
By increasing the spatial resolution of remote sensing imaging sensors, the image analyzing paradigm is
moving towards the object based image analysis approaches, instead of single pixels. Among the common
segmentation algorithms, super-pixel methods are presenting themselves as the new tools in computer vision.
In this paper, the capabilities of a state-of-the-art super-pixel algorithm, namely called SLIC, is investigated for
creating image segments from ultra-high resolution remote sensing images. In our proposed method, square
and hexagonal super-pixels were formed and then DBSCAN clustering algorithm is employed to build image
segments from these pixels. The results were compared to image segments obtained from FNEA algorithm, a
well-known method for remote sensing image segmentation. Visual and quantitative evaluations demonstrate
the efficiency of proposed method.
Super-pixel, Segmentation, Ultra-high resolution Images, Remote Sensing.
http://jgit.kntu.ac.ir/article-1-426-en.html
http://jgit.kntu.ac.ir/article-1-426-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Effect of different SRFs on time series of spectral indices, between sentinel-2 and other sensors for the purpose of vegetation land cover monitoring
111
132
FA
Sadra
Imanyfar
University of Tehran
N
mahdi
hasanlou
University of Tehran
hasanlou@ut.ac.ir
Y
10.29252/jgit.5.1.111
Quality and quantity of vegetation land cover is considered as one of the important aspects of environment. Detection of trends in natural phenomena such as vegetation, requires long-term studies, more than lifetime of a satellite. On the other hand, combining data from different sensors could lead to formation of false changes. One of the main causes of false changes is different spectral sensitivity functions (SRFs), among sensors under study. In this regard, the impact of these factors should be eliminated or reduced as much as possible by a procedure named relative calibration which is the main goal of this research. There are similarities between Landsat satellites series and SPOT-5 with Sentinel-2 in many aspects, so MSI (the Sentinel-2’s sensor) has capacity for data continuity. In this study, by incorporating polynomial equations, Landsat sensors (OLI, ETM +, ETM) and SPOT-5 were calibrated relative to MSI. The combination of radiative transfer models; PROSPECT-4 for leaf and 4SAIL for canopy, were used to simulate 50000 top of canopy synthetic spectral signatures and then soil effect was combined with them using linear spectral mixture model. After all, 150000 signatures were simulated. These spectral signatures were transformed to equivalent reflectance values (Blue, Red, NIR and SWIR) and spectral indices (NDVI, EVI and NDWI). 80% of spectral signatures were selected randomly for solving relative calibration models. Also, for validation purpose, remained simulated (20%) and 38 top of canopy measured spectral signatures were used. According to the results, linear equation can model the difference (caused by SRF) between MSI and others quite well and there is no need for more complicated equations. In general, results of this research show high and acceptable correlation for all reflectance bands and indices. It is more necessary to perform a relative calibration pre-processing step for EVI time series. Amongst reflectance bands, NIR has the highest continuity
Relative calibration, Spectral response function, Sentinel-2, Vegetation cover monitoring
http://jgit.kntu.ac.ir/article-1-427-en.html
http://jgit.kntu.ac.ir/article-1-427-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
5
1
2017
6
1
Support Vector Random Machines (SVRMs), A Optimum
Multiclassifier for Big Data
133
152
FA
mohsen
jafari
University of Tehran
jafarim@ut.ac.ir
Y
Mehdi
Akhoundzadeh
University of Tehran
N
10.29252/jgit.5.1.133
Although, the distinction between the land cover classes was increased in large feature space of remote sensing images, but
the low number of training data prevent this. In order to solve this problem, ensemble classification methods can be used
instead of individual classifiers. In this paper, a new method for ensemble support vector machine was proposed called
“Support Vector Random Machines (SVRMs(”. In proposed method, bootstrap was produced using modification of
training data and feature space. Simultaneous boosting SVM was used for basic classifiers. Then, classification map was
resulted using SVM fusion of basic classifier. Hyperspectral and Polarimetric SAR data was chosen for evaluation
performance of the SVRMs. Experiments were evaluated from three different points of view: First, evaluation against other
ensemble SVM methods; second, evaluation against various feature selection methods in classification and third,
evaluation against the various basic and new classification methods. As the results, proposed method is 16% better than the
individual SVM classifier in hyperspectral data and this is 10% in PolSAR data. Also, the classification results of SVRMs
in various classes compared to other SVM ensemble method were improved. The results reported from the proposed
method compared to the other feature selection method (Genetic Algorithm) has the effectual performance in classification.
The results show that the proposed method presents a better performance compared to the basic classification methods
(maximum likelihood and wishart) and advanced classification (random forest and neural network).
Support Vector Machine (SVM), Ensemble method, Feature space, Bootstarp, Aggregation
http://jgit.kntu.ac.ir/article-1-428-en.html
http://jgit.kntu.ac.ir/article-1-428-en.pdf