ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY
Year:2018 | Volume:6 | Issue:1 |Papers Count:10

The main problems of hyper spectral data are large number of bands, high dependency between them and different signal to noise ratio in each band. To reduce dimensions of the feature space, minimizing noise and spectral dependence between bands, the MNF method has been applied to achieve better results in this paper. By applying this algorithm, the 144 bands of hyper spectral data were reduced to 19 suitable bands. Then from LiDAR data, the image height and intensity of the return signal received from the first and the last pulse of the laser were examined by LiDAR sensor. At last, the 19 spectral bands extracted from hyper spectral data have been fusion with 4 images of LiDAR data at the pixel level to create 23 suitable spectral bands. In order to detect and extract any study feature of the area on 23 spectral bands, seven different SVM methods were applied and finally by majority voting in the decision-making level between 7 obtained results, the class of each pixel was turned out. Morphology closing transform for repairing buildings and Hough transform for reconstructing the network effects of the fragmentation of land transportation were used on the results of pixels basis SVM method to regulate man-made side structure as well as the individual pixels which reduced. The results in this paper indicates the 99. 52% overall accuracy and. 958 kappa efficiency which compared to the GRSS chosen institution method. 0. 6 Kappa coefficient has been improved. Used data are air-borne LiDAR and hyper spectral scenes requested and downloaded from the organized of a recent contest in data fusion domain.

The maximum coverage sensor deployment problem has attracted researchers of engineering sciences always as one of the fundamental phases in developing of communication and geospatial infrastructures. In this research, a novel strategy is proposed to tackle the maximum coverage robotic sensor deployment task in 3D vector spaces. For this purpose, first, a geometric algorithm is developed in order to detect the covered areas. The water cycle optimization algorithm is utilized to maximize the sensor coverage. Then, to avoid the problem of premature convergence to local optima and to improve the efficiency and searching potential on the problem, an improved water cycle algorithm with dynamic operations and fewer parameters is designed and developed. With regard to several scenarios with different spatial constraints, the efficiency of the proposed algorithm is compared to other methods based on robustness, running time, best and average of the coverage results, standard deviation, convergence speed, and wilcoxon statistical test. The assessment of the results reveals the superior performance of the proposed approach by success rate of 73% and coverage of 80% in a 3D vector space.

Differential Code Bias (DCB) of GPS satellites and receivers are one of the most important error sources in a positioning and ionosphere modeling using GPS code observations. International GNSS Services (IGS) compute and publishes the DCBs of GNSS satellites and its nework GNSS receivers as an ionosphere single layer model byproduct. Determination of Iranian Permanent GPS Network (IPGN) receivers DCBs independent of ionosphere single layer modeling is the aim of this paper. This method uses single difference observation geometry changes. Total electron content (TEC) differences of single difference observations can be considered as a linear function of its observation differences. When the distance between to receivers and satellite are equal, the effect of TEC will be removed from single difference observation and just the effect of different DCBs of two receivers will be remained in a single difference observation. The proposed method is implemented on a network which includes some IGS GNSS stations and derived DCBs are compared with IGS published DCBs for those stations. The maximum difference is 0. 6 nanosecond and the RMSE of the differences is 0. 4 nanosecond. This comparision shows the high efficiency of proposed method for determination of GPS receivers DCBs. Then IPGN GPS receivers DCBs are computed which can be published as a product for users.

Transport networks are the main foundation of continuous development of regions due to its importance to economic, industry, political, and even military. Public transportation network becomes significantly important before and after earthquakes, rescue operations, and displacement and evacuation of victims. Hence, geometric network design and planning of urban transport to reduce potential injuries seem vital. The Kerman city due to its geographical location and being trapped within earthquake faults is vulnerable. In this work, we studied the Kerman urban transport network vulnerability against earthquakes resistance based on criteria Earth and using GIS-AHP by defining the reference database for urban routes, to serve and to drain injuries faster during or after earthquakes. In descriptive tables, the layer of city routes, vulnerability of fields along with route name, identification code, being a one-way or two-way route, classification, and route lengths are documented. When required, by applying network analysis such descriptive tables can be used to find the best route to rescue victims. Results show that the most vulnerabile transport networks in the Kerman city are in regions 1and 3 which is due to accumulation of vulnerable urban facilities and low-resistant ground in these areas. Hence, these regions should be considred as highest priority in planning.

Land surface temperature is one of the most important parameters in environmental studies. Having satellite imageries with spatiotemporal resolution leads to better interpretation, analysis and clarity of images; therefore the best way to solve this problem is to combine images with high spatial and temporal resolution. There is no satellite that captures thermal band with both spatial and temporal resolution simultaneously due to technical difficulties and considerable cost. Therefore, the aim of this article is using SADFAT algorithm for providing land surface temperature images with spatial resolution of Landsat and temporal resolution of Modis. This paper uses seven dates of Modis and Landsat including 24th May, 9th June, 11th July, 27th July, 12th Aug, 28Aug and 13th September of Salman Farsi sugar cane Industry. The results are evaluated with four indexes of correlation coefficient, Average difference, Mean Absolute Error and Universal Image Quality Index. Comparison of predicted and observed images indicate that the value of indexes correlation coefficient, Root Mean Square Eroor, Mean Absolute Error and Universal Image Quality Index are between 0. 85-0. 99, 0. 73-1. 32, 0. 58-1. 73, 0. 9124-0. 9973. The results showed high, reliable and precession of SADFAT algorithm for providing daily land surface temperature with spatial resolution of Landsat in case study.

Increasing the population and urban development is one of the most important human actions that cause changes on the face of the earth, especially in the developing countries, which is more. This process can cause devastating effects such as social, economic and biophysical. The harmful effects include; loss of agriculture lands, pasture and forest, change the pattern of the water, which somehow is associated with the changing patterns of land use and land cover. Land use and land cover changes as a basic factor in the changes of the Environment Act and converted into crisis. Identifying and evaluating the potential land-use patterns is essential, that if done on timely and with the high precision, it can help the planners and managers of relevant organizations for more conscious decision and making optimum use of resources in order to prevent the crisis. That would only be possible with the change detection. The hyperspectral images, due to having high spectral resolution, improved results of changes detection, provide more details of the changes. The main purpose of this research is to improve the process of land-use changes detection using spectral angle mapper algorithm, expectation maximization based on kernel based with hyperspectral imagery. The most important advantage of this method are as follow: unsupervised, no need to setting parameters of the knowledge basis, high precision and low false alarms rate. To evaluate the ability of the proposed method, hyperspectral imagery received from agricultural fields of Hermiston in the United States that captured by Hyperion sensors were used. The results are a significant improvement with the use of the proposed method for change detection in the standard spectral angle-mapping model compared to the top so that the overall accuracy is 94%, the coefficient Kappa 0. 84 and false alarm rates of less than 6%.

In recent decades, close-range photogrammetry has been successfully used in various fields including industry, cultural heritage, health and civil engineering. This method as a tool for measuring deformation in industrial parts, has many advantages such as capability of real-time measurement, ease of observation, ability to achieve high accuracy and also capability of creating an archived observations for future processing. In this paper, close-range photogrammetry applied to measure a vehicle's body before and after collision to determine deformation on it. For this purpose, a measuring system is designed based on the core components of network and technical structure, taking observations, adjustment and calculation into account. Combined Photogrammetry Displacement Adjustment method (CPDA) has been applied in this research to achieve high accuracy in the measurement of displacement. The results revealed the ability of this method to achieve an accuracy of less than 1 mm in measuring the deformation of vehicle’ s body. Also this method is superior to other methods in terms of cost, speed and ease of measurements.

Considering rigidity in General and Detail urban plans, modeling the effects of changes mathematically would be worth in such plans. Investigating the effects of urban land use changes in the arrangement of other land uses and designing criteria such as consistency, dependency, suitability and per capita demand, always is a multi-objective and NP-hard problem. Due to the variety of urban land uses and their complex relationships with each other, many possible arrangements of land uses can be suggested. In this study, the main target is obtaining the effects of changes in one or more land uses in the arrangement of the other land uses considering three objective functions and one criteria simultaneously. These objective functions include consistency, dependency and suitability. Moreover, per capita demand assume as criteria in this research. To do so, MOEA/D algorithm is applied. Results demonstrate that the solutions are acceptable in the test of meta-heuristic algorithms. Furthermore, the results of the algorithm shows more optimized answer than current status. It is notable to say that the run time of this algorithm is considerably lower than other MOEAs like NSGA-II. Besides, the search space of MOEA/D is more expanded than NSGA-II.

The multi-temporal Remote sensing data are unique tools for monitoring and detecting land cover change over time. Radiometric and geometric consistency among these multi-temporal data are difficult to maintain, due to variations in sensor characteristics and view, solar angle, and atmospheric conditions, and these variations can obscure surface change detection. The radiometric normalization and geometric co-registration of multi-temporal satellite imagery of the same terrain is often necessary for land cover change detection, e. g., relative differences. In previous studies, in order to obtain radiometric correction of multi temporal imagery, the ground reference data or pseudo-invariant features (PIFs) were used. Using the ground reference data collection is costly and difficult to acquire for most satellite remotely sensed images and the selection of PIFs is generally subjective and need the user’ s supervision. In this research, we demonstrate a method for radiometric normalization and geometric co-registration between multi temporal images of the Alam-chal Glacier. The selection of PIFs has been done statistically, and the satellite images are normalized radiometrically to a common scale. In order to image co-registration, first the noise was removed and then repeatedly two images were registered using polynomials models and image matching. The proposed method was evaluated by histogram comparison, statistical parameters and independent check points. The results show that the statistical parameters of two image are nearly the same and the total RMSE of check points was 0. 52 pixel.

The atmosphere is a complicated and dynamic system containing natural gases as well as some extra gases produced through different sources. Concentration of suspended particles in the atmosphere is one of the most important indicators of air pollution. Tehran is among the most polluted cities in the world. Being able to determine the amount of pollution in the city’ s air, may lead to strategies being adopted for reduction of its negative effects. Commonly, measurement of the air pollution is carried out by gauges installed in stations all around the city. These limited number of gauges can precisely measure pollution within the station zone. However, the measured data is not valid for the regions far from stations. NO2 is one of the most important factors in the air pollution; hence this study attempts to determine it in urban areas using remote sensing. OMI images are routinely providing air pollution data on a daily basis. These images give the amounts of pollution in large pixels which are not appropriate for urban areas. In this study, the concurrent images of MODIS and OMI were used in order to find a relation between pollution and reflectance in different bands. At first, the relationship between pollution and reflectance in industrial areas and large cities were determined. Then different combinations of equations were considered for MODIS bands and the best combination was chosen. At the end, distribution image of pollution was obtained in the city. Evaluation of this equation shows acceptable accuracy in prediction of NO2 by MODIS images. In addition, critical and highly polluted areas were determined by accumulation of air pollution images on different days. At the end, data of ground stations were utilized in order to evaluate acquired results (RMSE=0. 29 and RRMSE=44. 3%). The model showed small relative errors (15%) for large amounts of NO2 and huge errors (100%) for low amounts of pollution.