طبقهبندی کاربری و پوشش زمین با ترکیب الگوریتمهای GLCM، SNIC و یادگیری ماشین در سامانۀ گوگل ارث انجین (مطالعۀ موردی: بخشی از اراضی شمال مهاباد، آذربایجانغربی) | ||
| نشریه سنجش از دور و GIS ایران | ||
| دوره 17، شماره 1 - شماره پیاپی 65، 1404، صفحه 41-60 اصل مقاله (2 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.48308/gisj.2023.233271.1181 | ||
| نویسنده | ||
| مهدی نادری* | ||
| گروه سنجش از دور و GIS ، دانشگاه تربیت مدرس، تهران، ایران | ||
| چکیده | ||
| سابقه و هدف: در دهههای گذشته، دادههای سنجش از دور با موفقیت برای استخراج اطلاعات و تغییرات کاربری و پوشش زمین (LULC) در سطوح گوناگون، از مقیاس محلی تا جهانی، به کار رفته است. پایش دقیق و منظم این تغییرات در برنامهریزی شهری، کشاورزی دقیق و مدیریت پایدار منابع زمین ضرورت دارد. فراهمی دادههای سنجش از دور با ارائۀ سطوح بیسابقهای از جزئیات مکانی و همچنین توسعۀ الگوریتمهای طبقهبندی تصاویر ماهوارهای، باعث شده است که رویکردهای شیءگرا، در مقایسه با رویکردهای معمول، در طبقهبندی کاربری و پوشش زمین کاربرد بیشتری پیدا کنند. بدینمنظور، در این مطالعه، رویکردی شیءگرا با ترکیب الگوریتمهای GLCM، SNIC و یادگیری ماشین مطرح شده که هدف از آن طبقهبندی کاربری و پوشش زمین بخشی از اراضی شمال مهاباد در آذربایجانغربی، با استفاده از تصاویر ماهوارهای سنتینلـ 2 در سال 2019 در سامانۀ گوگل ارث انجین بوده است. مواد و روشها: روش انجام شدن این پژوهش بهگونهای است که ابتدا مجموعه دادۀ اولیه، شامل باندهای هدف تصاویر سنتینلـ 1 و سنتینلـ 2، مدل رقومی سطح زمین ALOS و شاخصهای NDVI، BSI، SAVI و توان بازپراکنش کل (TSP) آمادهسازی شد. در مرحلۀ دوم، با اتخاذ دو رویکرد پیکسلپایه و شیءگرا و الگوریتم جنگل تصادفی، کاربری و پوشش زمین طبقهبندی شد و نتایج حاصل از آنها، برای تبیین بهترین رویکرد ازنظر دقت کلاسهای گوناگون، مقایسه شد. در رویکرد شیءگرا، معیارهای بافتی با اعمال ماتریس وقوع توأم گامهای خاکستری (GLCM) روی مجموعه دادۀ اولیه استخراج شد و با توجه به افزایش تعداد باندها روش تحلیل مؤلفههای اصلی (PCA)، برای کاهش ابعاد تصویر، به کار رفت. در گام آخر، با ترکیب لایۀ PC1 و لایۀ قطعهبندی حاصل از الگوریتم خوشهبندی سادۀ غیرتکراری (SNIC)، الگوریتم جنگل تصادفی بهمنظور تهیۀ نقشههای کاربری و پوشش زمین محدودۀ مطالعاتی در نظر گرفته شد. نتایج و بحث: تحلیل معیارهای ارزیابی صحت نشان داد که رویکرد شیءگرا با صحت کلی و ضریب کاپای معادل 86/40% و 0/8307، در مقایسه با رویکرد پیکسلپایه با صحت کلی و ضریب کاپای 82/73% و 0/8028، نتایج بهتری را در طبقهبندی کاربریهای متفاوت اراضی منطقۀ مورد مطالعه داشته است. نتایج معیارهای ارزیابی صحت نشان داد صحت تولیدکنندۀ اغلب کلاسهای کاربری، بهجز ذرت، سبزیجات آبی پاییزه و گندم و جو آبی، در رویکرد شیءگرا بیشتر از روش پیکسلپایه است و دقت طبقهبندی آنها بالاتر از 90% بوده است. علاوهبراین، کاربریهای/ پوششهای پهنۀ آبی، ساختهشده، ذرت و چغندرقند بیشترین صحت کاربر را در نقشۀ کاربری و پوشش زمین شیءگرا به خود اختصاص دادهاند. نتیجهگیری: یافتههای تحقیق نشان دادند که تعیین مناسب اندازۀ سوپرپیکسل الگوریتم خوشهبندی SNIC و بهکارگیری معیارهای بافتی GLCM بهطور مؤثری عملکرد رویکرد پیشنهادی را در طبقهبندی کاربری و پوشش زمین، بهبود میبخشد. | ||
| کلیدواژهها | ||
| طبقهبندی شیءگرا؛ جنگل تصادفی؛ شاخصهای طیفی؛ دادۀ رادار و اپتیک؛ اندازۀ سوپرپیکسل | ||
| عنوان مقاله [English] | ||
| Land Use and Land Cover Classification by Combining GLCM, SNIC, and Machine Learning Algorithms in Google Earth Engine Environment (Case Study: Part of the Lands of North Mahabad, West Azerbaijan) | ||
| نویسندگان [English] | ||
| Mahdi Naderi | ||
| Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Introduction: In recent decades, land use and land cover changes information has been successfully derived from remote sensing data at various levels, from local to global scale. Accurate and frequent monitoring of these changes is required for urban planning, precision agriculture, and sustainable management of land resources. The availability of remote sensing data by providing different levels of spatial details, as well as the development of satellite image classification algorithms, has made object-oriented approaches more useful in land use and land cover (LULC) classification compared to traditional approaches. Therefore, in this study, an object-oriented approach using a combination of GLCM, SNIC, and machine learning algorithms is presented to classify the LULC of a part of the lands of North Mahabad, West Azerbaijan, in 2019 using satellite images in Google Earth Engine. Data and Methods: For this purpose, after preparing the initial dataset, which contains the bands of Sentinel-1 and Sentinel-2 images, the ALOS digital surface model, and NDVI, BSI, SAVI, and total scattering power (TSP) indices, two pixel-based and object-oriented approaches, as well as the random forest algorithm, were used to classify land use and land cover, and their results were compared to explain the best approach in terms of the accuracy of the various classes. In the object-oriented approach, textural measures were extracted by applying the GLCM matrix to the initial dataset. Due to the increase in the number of bands, the PCA method was used to reduce the dimensions of the image. Finally, by combining the PC1 layer and the segmentation layer obtained from the SNIC algorithm, the random forest algorithm was considered to produce land use and land cover maps of the study area. Results and Discussion: According to the research findings, the object-oriented approach performed better than the pixel-based approach in classifying various land use classes in the study area, with an overall accuracy and kappa coefficient of 86.40% and 0.8307, respectively, compared to 82.73% and 0.8028. The results of the accuracy evaluation criteria showed that the producer accuracy of most of the classes except for corn, fall irrigated vegetables, wheat, and barley irrigated in the object-oriented approach was higher than the pixel-based method, and their classification accuracy was more than 90%. Additionally, water, build-up, corn, and sugar beet classes have the highest user accuracy in the object-oriented LULC map. Conclusion: The findings showed that the appropriate determination of the super-pixel size of the SNIC clustering algorithm and the use of GLCM texture criteria effectively improved the performance of the proposed approach in land use and land cover classification | ||
| کلیدواژهها [English] | ||
| Object-oriented Classification, Random forest, Spectral indices, Radar and optic data, Super-pixel size | ||
| مراجع | ||
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