استخراج ساختمان ها در نواحی شهری مبتنی بر داده های سری زمانی راداری و اپتیکی با استفاده از سامانه گوگل ارث انجین

استخراج اطلاعات دقیق مربوط به موقعیت، تراکم و توزیع ساختمان‌ها در محدوده شهری از اهمیت بسیار بالایی برخوردار است که در کاربردهای مختلفی مورد استفاده قرار می‌گیرد. سنجش از دور یکی از کارآمدترین تکنولوژی‌های تهیه نقشه است که در مناطق وسیع، با سرعت بالا، هزینه مقرون به صرفه و با به‌کارگیری داده‌های به‌روز مورد استفاده قرار می‌گیرد. تاکنون روش‌ها و داده‌های متعددی برای این منظور مورد استفاده قرار گرفته است. در این راستا، در تحقیق حاضر از یک روش نیمه‌خودکار به‎منظور تهیه نقشه محدوده شهری و ساختمان‌های شهر تبریز و از تصاویر ماهواره‌ای سنتینل1 و 2 در سامانه گوگل ارث انجین استفاده شد. برای این منظور، بعد از فراخوانی تصاویر و اعمال پیش‌پردازش‌های لازم در موتور مجازی، نقشه مناطق شهری اولیه و ساختمان‌هایی با پتانسیل بالا از تصاویر سنتینل1 تولید شد. در مرحله بعد، به‌منظور حذف ویژگی‌های مزاحم و استخراج مناطق شهری ثانویه، شاخص‌های طیفی از تصاویر سنتنیل2 استخراج شد. سپس برای آستانه‌گذاری ویژگی‌ها از آستانه‌گذاری هیستوگرام به روش تک مدی استفاده شد. در نهایت، با ادغام نقشه ساختمان‌های با پتانسیل بالا و نقشه مناطق شهری ثانویه، نقشه نهایی تولید و مورد ارزیابی قرار گرفت. نتایج حاصل، نشان‌دهنده صحت کلی 90/11 درصد و ضریب کاپای 0/803 می‌باشد. براساس مقایسه‌های کمّی و کیفی انجام شده، روش پیشنهادی از عملکرد مطلوبی برخوردار می‌باشد. از مهم‌ترین مزایای روش پیشنهادی می‌توان به رایگان بودن داده‌ها و متن‌باز بودن سامانه گوگل ارث انجین اشاره کرد. بنابراین، می‌توان نتیجه گرفت که استفاده همزمان از داده‌های سنجش از دور راداری و اپتیکی در محیط سامانه گوگل ارث انجین، پتانسیل بسیار بالایی در متمایز کردن ویژگی‌ها و تهیه نقشه ساختمان‌ها دارد.

Buildings extraction in urban areas based on the radar and optical time series data using Google Earth Engine

Extended Abstract1 IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, costeffectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), objectbased methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the timeseries images. In such a situation, the use of the Google Earth Engine (GEE) as a webbased RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semiautomatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel1 and sentinel2 data, which belongs to the European Space Agency (ESA), has been utilized. 2 Materials & Methods21 Study AreaThe study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 22 DataVarious data sources have been used in this study, including Sentinel1, Sentinel2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using HighResolution Google Earth Imagery (GEI) in two classes: urbanresidential (buildings) and nonresidential areas (vegetation, soil, road, water and etc.).23 MethodologyThe goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and preprocessing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and HighPotential Buildings (HPB) was produced from Sentinel1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Builtup Index (NDBI), and Buildup Extraction Index (BUEI) were extracted from Sentinel2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and nonresidential areas. The samples were selected randomly and 400 points were collected for each residential and nonresidential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used. 3 Results & DiscussionAccording to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance. 4 ConclusionDue to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semiautomatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel1 and Sentinel2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE WebBased platform has a very high potential in distinguishing features and building mapping.