Geographic Information System (GIS)
Abolfazl Ghanbari; Mostafa Mousapour; Habil Khorrami hossein hajloo; Hossein Anvari
Abstract
Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of ...
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Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.Materials & Methods: The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.Conclusion:Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.
Hassan Lashkari; Ghasem Key Khosravi
Volume 19, Issue 75 , November 2010, , Pages 60-66
Abstract
Strong winds have been named storms, which, in various forms and at high speed, blow over for a short time and are usually accompanied by unstable air. If unstable air is humid, a thunderstorm, and if it is dry, a dust storm will form (Alijani, 2000). In all stations of Hamadan province the majority ...
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Strong winds have been named storms, which, in various forms and at high speed, blow over for a short time and are usually accompanied by unstable air. If unstable air is humid, a thunderstorm, and if it is dry, a dust storm will form (Alijani, 2000). In all stations of Hamadan province the majority of thunderstorms occur in the spring. In this season, the greatest number of thunderstorms have occurred due to increased solar radiation energy, which is characterized by significant atmospheric moisture content due to evaporation and vegetation cover, wet lands and transitional thermodynamic systems from the west and southwest of the country. The fall season is second in terms of the number of Thunder storms. In this season, due to the high radiation energy and the arrival of unstable systems with an appropriate moisture source, the number of thunderstorms is significant. The smallest number of thunderstorms belongs to winter. The maximum average of dust storms occurs mainly in dry areas without vegetation, and the number of these storms is less in the mountainous areas of Hamadan. The seasons with most frequent storms in the districts of Ekbatan, Hamadan, Noghe and Malayer are spring and summer, and at Nahavand station the summer season. The study of the trend of hourly changes in storms frequency shows that the maximum occurrence of these storms is from 9 am on, and at 6 p.m. their intensity and frequency decreases, and in all stations the frequency of these storms is higher in days than in nights. According to the conducted surveys, the most activity of dust storms is in the spring and summer. Although during this period of time the ground for local instability is provided due to the high radiation energy and suitable topographic conditions, but the energy of the systems is evacuated as dust storms because of the lack of adequate moisture.
