تهیة نقشة اراضی کشاورزی با استفاده از تلفیق روش‌های قطعه‌بندی و طبقه‌بندی در گوگل ارث انجین | ||
| نشریه سنجش از دور و GIS ایران | ||
| مقاله 1، دوره 14، شماره 1 - شماره پیاپی 53، 1401، صفحه 1-20 اصل مقاله (2.15 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.52547/gisj.14.1.1 | ||
| نویسندگان | ||
| علیرضا طاهری دهکردی* 1؛ محمدجواد ولدان زوج2؛ علیرضا صفدری نژاد3 | ||
| 1دانشجوی کارشناسی ارشد فتوگرامتری، دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| 2استاد گروه فتوگرامتری و سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسی | ||
| 3استادیار گروه ژئودزی و مهندسی نقشه‌برداری، دانشگاه تفرش | ||
| چکیده | ||
| تهیة نقشة اراضی کشاورزی یکی از لایه‌های اطلاعاتی مورد نیاز در مدیریت این زمین‌ها محسوب می‌شود. چنین نقشه‌هایی امکان پایش مستمر زمین‌های کشاورزی را در طول دورة کشت، فراهم می‌کنند. در این مطالعه، راهکاری به‌منظور تولید نقشة اراضی کشاورزی شهرستان شهرکرد، در دو کلاس زراعی و غیرزراعی، با استفاده از سری‌زمانی شاخص‌های مستخرج از تصاویر سنتینل‌ـ 2 داده شده است. ازآن‌جا‌که استفاده از منابع دادة حجیم یکی از موانع بهبود روش‌های مبتنی‌بر سری زمانی تصاویر ماهواره‌ای به‌شمار می‌رود، در این پژوهش از بستر پردازشی گوگل ارث انجین استفاده شده است. روش مطرح‌شده برمبنای تلفیق نتایج طبقه‌بندی نظارت‌شدة پیکسل‌مبنا با نتایج قطعه‌بندی عمل می‌کند؛ به‌نحوی‌که ابتدا داده‌های آموزشی طبقه‌بندی نظارت‌شده، طی یک فرایند پالایشی سخت‌گیرانه، بدون نیاز به عملیات میدانی فراهم می‌شوند. سپس با محاسبة تفکیک‌پذیری دو کلاس هدف در سری زمانی هر شاخص، شاخص‌های بهینه انتخاب می‌شود. در نهایت، با تلفیق نتایج روش‌های قطعه‌بندی و طبقه‌بندی براساس آرای به‌دست‌آمده از نتایج طبقه‌بندی، به هر قطعة تصویری کلاس زراعی یا غیرزراعی نسبت داده می‌شود. این اقدام، علاوه‌بر دخالت‌دادن اطلاعات مکانی اعم از لبه‌ها و مجاورت‌های مکانی، توانسته است نویز و نتایج متخلخل طبقه‌بندی پیکسل‌مبنا را بهبود بخشد و دقت کلی نقشة نهایی را از 7/90 به 05/96 افزایش دهد. همچنین دقت کاربر دو کلاس زراعی و غیرزراعی به‌ترتیب 27/3 و 97/7% بهبود را نشان می‌دهند. | ||
| کلیدواژه‌ها | ||
| طبقه‌بندی؛ قطعه‌بندی؛ تلفیق؛ گوگل ارث انجین؛ اراضی کشاورزی | ||
| عنوان مقاله [English] | ||
| Cropland Mapping through Integration of Segmentation and Classification Techniques in Google Earth Engine | ||
| نویسندگان [English] | ||
| Alireza Taheri Dehkordi1؛ Mohammad Javad Valadanzouj2؛ Alireza Safdarinezhad3 | ||
| 1M.Sc. Student in the Dep. of Remote Sensing and Photogrammetry, K.N. Toosi University of Technology | ||
| 2Prof. of Dep. of Remote Sensing and Photogrammetry, K.N. Toosi University of Technology | ||
| 3Assistant Prof., Dep. of Geodesy and Surveying Engineering, University of Tafresh | ||
| چکیده [English] | ||
| Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural and non-agricultural classes is presented using the time series of different extracted indices from Sentinel-2 images. Since the use of large data sources is one of the obstacles to the development of methods based on the time series of satellite images, the Google Earth engine processing platform has been used in this study. The proposed method is based on integrating supervised pixel-based classification results with segmentation results. First, training data of supervised classification is provided in a rigorous refining process without the need of collected data from field surveys or interpretation of high-resolution satellite images. Then, by calculating the separability of the two target classes in the time series of each index, the optimal indices are selected. Finally, by combining the results of segmentation and classification methods based on the votes obtained from the classification results, agricultural or non-agricultural class is assigned to each of the image segments. In addition to incorporating spatial information including edges and spatial proximity, this method has been able to improve the noise and porous results of pixel-based classification and has increased the overall accuracy of the final map from 90.7% to 96.05%. Also, user accuracy of both agricultural and non-agricultural classes show an improvement of 3.27 and 7.97%, respectively. | ||
| کلیدواژه‌ها [English] | ||
| Classification, Segmentatiom, Integration, Google Earth Engine, Croplands | ||
| مراجع | ||
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