استخراج تاج‌پوشش درختان شهری با روش طبقه‌بندی شیء‌پایه و الگوریتم‌های یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تربیت مدرس

2 استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه تربیت مدرس، تهران

3 استادیار گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشگاه شهید بهشتی، تهران

چکیده

آگاهی از میزان تاج‌پوشش درختان در مناطق شهری به‌علت تأثیرات آن در کاهش آلودگی‌‌های هوا، کاهش آلودگی صوتی، جلوگیری از باد، ذخیرة نزولات آسمانی، و کاهش و کنترل رواناب‌های شهری بسیار ضروری است. ازآن‌جا‌که استخراج تاج‌پوشش درختان با روش‌های دستی بسیار وقت‌گیر و پرهزینه است، تکنیک‌های سنجش از دور می‌توانند ابزار مناسبی برای تأمین این داده‌ها باشند. در طبقه‌بندی شیء‌پایه، انتخاب پارامترهای بهینة قطعه‌بندی، به‌ویژه پارامتر مقیاس، اهمیت بسیاری دارد و معمولاً با شیوة آزمایش و خطا تعیین می‌شود که کاملاً تجربی است. بنابراین، یکی از اهداف این پژوهش انتخاب مقیاس بهینة قطعه‌بندی به‌صورت خودکار است. همچنین، پس از استخراج قطعات، لازم است با یک روش طبقه‌بندی، قطعات استخراج‌شده تعیین کاربری/ پوشش زمین شوند و در این زمینه، انتخاب نوع روش طبقه‌بندی در نتیجة نهایی طبقه‌بندی شیء‌پایه بسیار اهمیت دارد. ازاین‌رو، پس از قطعه‌بندی با استفاده از داده‌های لیدار و تصاویر هوایی از شهر واهینگن در آلمان و تعیین ویژگی‌های مهم مستخرج از قطعات، با استفاده از روش انتخاب ویژگی برمبنای جنگل تصادفی، قطعات مربوط به تاج‌پوشش درختان از سایر قطعات تفکیک شد. این کار با بهره‌گرفتن از شیوه‌های یادگیری ماشین شامل ماشین بردار پشتیبان، جنگل تصادفی و درخت تصمیم‌گیری صورت گرفت. نتایج نشان‌دهندة برتری الگوریتم ماشین بردار پشتیبان، به‌منزلة برترین الگوریتم طبقه‌بندی‌کننده، و مقیاس 25، به‌منزلة بهترین مقیاس انتخابی، بود و در نهایت، الگوریتم‌های ماشین بردار پشتیبان، جنگل تصادفی و درخت تصمیم‌گیری در مقیاس 25، به‌ترتیب، با شاخص‌های کیفیت 79.90 و 79.16 و 76.90 توانستند تاج‌پوشش درختان را استخراج کنند.

کلیدواژه‌ها

عنوان مقاله [English]

Urban Tree Canopy Mapping Using Object Oriented Classification and Machine Learning Algorithms

نویسندگان [English]

  • Nahid Haghshenas 1
  • Ali shamsoddini 2
  • Hossein Aghighi 3
1 M.Sc. Student, Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran
2 Assistant Prof., Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran
3 Assistant Prof., Dep. of Remote Sensing and GIS, Shahid Beheshti University, Tehran
چکیده [English]

It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.

کلیدواژه‌ها [English]

  • Urban tree canopy cover
  • Object oriented method
  • Scale parameter
  • Machine learning algorithms

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نشریه سنجش از دور و GIS ایران
دوره 13، شماره 1 - شماره پیاپی 49
اردیبهشت 1400
صفحه 17-32

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