طبقهبندی شیءگرای مناطق شهری با تلفیق تصاویر سنتینلـ 1 و سنتینلـ 2 | ||
| نشریه سنجش از دور و GIS ایران | ||
| مقاله 7، دوره 14، شماره 3 - شماره پیاپی 55، 1401، صفحه 105-121 اصل مقاله (2.85 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/gisj.14.3.105 | ||
| نویسندگان | ||
| رضا شاه حسینی* 1؛ کمال عزیزی2؛ ارسطو زارعی2؛ فاطمه مرادی2 | ||
| 1استادیار دانشکدة مهندسی نقشهبرداری و اطلاعات مکانی، دانشکدگان فنی دانشگاه تهران | ||
| 2کارشناس ارشد دانشکدة مهندسی نقشهبرداری و اطلاعات مکانی، دانشکدگان فنی دانشگاه تهران | ||
| چکیده | ||
| نقشههای کاربری زمین، توزیع فضایی منابع طبیعی، مناظر فرهنگی و سکونتگاههای انسانی را توصیف میکنند که بهمنزلة ابزار برنامهریزی مهمی برای تصمیمگیرندگان عمل میکند؛ بنابراین دقت نقشههای حاصل از طبقهبندی تصاویر ماهوارهای در عدم قطعیت بهمنظور مدیریت شهری بسیار تأثیرگذار است. با توجه به کیفیت یکنواخت تصاویر در مناطق بزرگ در مراحل زمانی منظم، تصاویر سنجش از دور ورودی ضروری در تولید نقشههای کاربری زمین شمرده میشوند. هدف اصلی از این تحقیق پیشنهادکردن روشی بهمنظور ایجاد نقشة پوشش اراضی دقیق در مناطق شهری، با استفاده از تلفیق دادههای سنتینلـ 1 و سنتینلـ 2 است. به این منظور، ویژگیهای ضریب بازپراکنش راداری VV و دو پارامتر حاصل از روش تجزیة H-α (آنتروپی، آلفا) از تصاویر راداری سنتینلـ 1 و ویژگیهای باند آبی، سبز، قرمز، شاخصهای NDVI، NDWI، MNDWI و SWI از تصاویر اپتیک سنتینلـ 2 استخراج و به منزلة مؤلفههای تأثیرگذار در طبقهبندی منطقة شهری استفاده شد. در این مطالعه، با هدف جداسازی مناطق کشاورزی از سایر پوششها، از شاخص رطوبت SWI استفاده شد. همچنین دادههای ارتفاعی برای تفکیک بهینة کلاسهای پیچیده با توپوگرافی متفاوت بهکار رفت. استخراج شاخصهای تأثیرگذار از این دو مجموعه داده، در رویکردی شیءگرا مبتنی بر الگوریتمهای ماشین بردار پشتیبان و جنگل تصادفی، برای طبقهبندی کاربری زمین ارزیابی شد. نتایج نشان داد که بهکارگرفتن ویژگیهای استخراجشده از تصاویر راداری و اپتیک بهطور همزمان، در روش طبقهبندی شیءگرا، میتواند ویژگیهای شیء را بهطور کامل در ناحیة مورد مطالعه استخراج کند. در مورد هر دو الگوریتم طبقهبندی، زمانی که از دادههای اپتیک و راداری بهطور همزمان استفاده شد، دقت کلی طبقهبندی افزایش داشت. در مورد روش جنگل تصادفی که بیشترین دقتها را دربر داشت، دقت کلی برای رویکرد ترکیب دادههای راداری و اپتیک بهمیزان 13 و 5%، بهترتیب به نسبت رویکرد تنها ویژگی راداری و تنها رویکرد ویژگی اپتیک، افزایش پیدا کرده است. همچنین اختلاف معنیداری در دقت طبقهبندی، در تمامی سطوح، بین الگوریتم طبقهبندی ماشین بردار پشتیبان و جنگل تصادفی دیده میشود. نتایج نشان داد که دقت کلی درمورد روش طبقهبندی جنگل تصادفی و ماشین بردار پشتیبان بهترتیب برابر 3/83 و 8/79% و ضریب کاپا بهترتیب 72/0 و 68/0% بوده است. | ||
| کلیدواژهها | ||
| سنجش از دور؛ سنتینلـ 1؛ سنتینلـ 2؛ طبقهبندی مبتنی بر شیء؛ ماشین بردار پشتیبان؛ جنگل تصادفی | ||
| عنوان مقاله [English] | ||
| Object-Oriented Classification of Urban Areas Using a Combination of Sentinel-1 and Sentinel-2 Images | ||
| نویسندگان [English] | ||
| Reza Shahhoseini1؛ Kamal Azizi2؛ Arastou Zarei2؛ Fatemeh Moradi2 | ||
| 1Assistant Prof., School of Surveying & Geospatial Engineering, College of Engineering, University of Tehran | ||
| 2M.Sc. Student, School of Surveying & Geospatial Engineering, College of Engineering, University of Tehran | ||
| چکیده [English] | ||
| Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the uniform quality of images in large areas at regular intervals, remote sensing images are essential for land use maps. The primary purpose of this study is to present a proposed method to create an accurate land cover map in urban areas using a combination of Sentinel-1 and Sentinel-2 data. For this purpose, the features of the backscattering coefficient VV and the two parameters obtained from the H-α decomposition method (entropy, alpha) of Sentinel-1 radar images and the features of the blue, green, red band, NDVI, NDWI, MNDWI, and SWI were extracted from Sentinel-2 Multispectral images and used as influential components to classify the urban area. To separate agricultural areas from other coatings, the SWI index was used. Elevation data have also been used to optimally distinguish complex classes with different topographies. We evaluated the extraction of effective indicators from these two datasets in an object-oriented approach based on support vector machine algorithms and random forest for land use classification. The results showed that using properties extracted from radar and Multispectral images simultaneously in the object-oriented classification method could altogether determinate the object's properties in the study area. When optical and radar data were used simultaneously for both classification algorithms, the overall accuracy classification increased. For the stochastic forest method, which provided the highest accuracy, the overall accuracy for the radar and optics data combination approach increased by 13% and 5%, respectively, compared to the radar feature approach and the optics feature approach alone. There was also a significant difference in classification accuracy at all levels between the support vector machine classification algorithm and the random forest. The results showed that the random forest classification method's overall accuracy and support vector machines were 83.3 and 79.8%, respectively, and the kappa coefficient was 0.72 and 0.68%, respectively. | ||
| کلیدواژهها [English] | ||
| Remote sensing, Sentinel-1, Sentinel-2, Object-oriented classification, Support vector machine, Random forest | ||
| مراجع | ||
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