مجله جنگل ایران

مجله جنگل ایران

ارزیابی روش قطعه‌بندی مبتنی برOctree در جداسازی ابر نقاط زمینی در داده‌های لیزر اسکنر دستی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 دانشجوی دکتری مدیریت جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران
2 استاد گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران
3 استادیار گروه جنگلداری و اقتصاد جنگل، دانشکدۀ منابع طبیعی، دانشگاه تهران، کرج، ایران
4 دانشیار گروه فتوگرامتری و سنجش ‌از دور، دانشکده مهندسی نقشه‌برداری و اطلاعات مکانی، دانشگاه تهران، تهران، ایران
5 کارشناس ارشد مهندسی نقشه‌برداری، شرکت نماپرداز رایانه، تهران، ایران
چکیده
مقدمه: یکی از مهم‌ترین داده‌های پایه برای برنامه‌ریزی و مدیریت جنگل، وجود مدل‌های رقومی ارتفاعی (DEMs) دقیق است. برای تهیۀ این داده‌ها از روش‌های مختلفی استفاده می‌شود که لیزر اسکنرهای دستی متحرک، از کاراترین و دقیق‌ترین آنهاست. داده‌های حاصل از لیزر اسکنرها به‌شکل ابر‌ نقاط خام هستند و باید پردازش‌هایی مختلفی برای آماده‌سازی این داده‌ها صورت گیرد. یکی از اصلی‌ترین مراحل پردازش ابر نقاط، جداسازی نقاط زمینی و غیر زمینی است، زیرا بروز خطا در این قسمت از پردازش‌ها به تهیۀ مدل‌های رقومی غیردقیق منجر خواهد شد. برای اجرای این مرحله، الگوریتم‌های گوناگونی مانند قطعه‌بندی مبتنی بر واکسل، فیلتر شبیه‌سازی پارچه و الگوریتم‌های مبتنی بر یادگیری عمیق ایجاد شده است. هدف این پژوهش، ارزیابی قابلیت الگوریتم قطعه‌بندی مبتنی بر Octree در جداسازی خودکار نقاط زمینی و غیر زمینی و تعیین مقادیر بهینۀ پارامترهای این الگوریتم در توده‌های درختی است.
مواد و روش­ها: پنج محدوده از باغ گیاه‌شناسی کرج به مساحت 2/7 هکتار که دربردارندۀ توده‌های درختی و دارای ساختار ناهمسال و چنداشکوبه است، انتخاب و با استفاده از لیزر اسکنر دستی ژئواسلم برداشت و بررسی شد. به‌منظور تهیۀ مرجعی مطمئن برای ارزیابی نتایج الگوریتم یادشده، جداسازی نقاط زمینی با دقتی زیاد و به‌طور دستی انجام گرفت و صحت نتایج در مقایسه با این مرجع واقعیت زمینی برپایۀ آماره‌های ضریب همبستگی متیوز، ضریب کاپا و IoU تعیین شد.
یافته­ها: میانگین حاصل از مقادیر آماره‌های ارزیابی کارایی مدل در پنج محدودۀ تحت بررسی نشان داد که الگوریتم قطعه‌بندی مبتنی بر Octree با ضریب همبستگی متیوز، ضریب کاپا و IoU به‌ترتیب 895/0، 891/0 و 902/0 صحت مطلوبی ارائه داده است. همچنین در محدوده‌های تحت بررسی مقدار بهینۀ ابعاد مکعب برای اجرای الگوریتم بازۀ 15 تا 22 سانتی‌متری تعیین شد.
نتیجه­گیری: می‌توان بیان کرد که الگوریتم قطعه‌بندی مبتنی بر Octree در صورت انتخاب مقادیر بهینۀ پارامترهای ورودی، از کارایی مطلوبی برای جداسازی نقاط زمینی و غیر زمینی در عرصه‌های جنگلی برخوردار است.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Evaluation of Octree-Based Segmentation (OBS) Method to Seperate Ground Point Based on the Handheld Laser Scanner Data

نویسندگان English

S.A. Naghibi Rad 1
A.A. Darvishsefat 2
P. Fatehi 3
M. Namiranian 2
M. Saadat Seresht 4
M. Boroumand 5
1 Ph.D. Student, Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
2 Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
3 Assistant Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran. I.R. Iran
4 Associate Prof., Dept. of Photogrammetry and Remote Sensing, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran. I.R. Iran
5 MSc in Surveying, Nama Pardaz Rayaneh Co. I.R. Iran
چکیده English

Introduction: Accurate digital elevation models (DEMs) are crucial for effective forest planning and management. Various methods exist for generating DEM data, with handheld mobile laser scanners being the most efficient and precise approach. The raw point clouds obtained from these scanners require several preprocessing steps, one of which involves separating ground and non-ground points. Errors in this part of the process can lead to the generation of an inaccurate digital model with high uncertainty and errors. Various algorithms, such as voxel-based segmentation, simulation filters, and deep learning-based approaches, have been developed for this purpose. This study evaluates the performance of the OBS algorithm in automatically separating ground points from non-ground points in handheld laser scanner data.
Material and Methods: The study area comprised five different sections within the Karaj Botanical Garden, covering a total area of 7.2 hectares. These areas contained forest stands characterized by heterogeneous structures and multi-story tree layers. Data were acquired using a handheld GeoSLAM laser scanner. To generate a reliable reference for evaluating the algorithm's results, ground points were manually separated. The performance of the algorithm was evaluated by comparing it with the manually separated ground truth using statistical metrics, including Matthew's correlation coefficient, Kappa coefficient, and Intersection over Union (IoU).
Results: The statistical metrics across the five study areas demonstrated the effectiveness of the OBS algorithm in separating ground points from non-ground points, with Matthew's correlation coefficient, Kappa coefficient, and IoU values of 0.895, 0.891, and 0.902, respectively. Additionally, the optimal voxel size for the algorithm was determined to be within the range of 15 to 22 centimeters.
Conclusion:We conclude that the OBS algorithm, when configured with optimal input parameters, provides high performance in automatically separating ground points from non-ground points, especially in heterogeneous forested environments. The importance of configuring the optimal input parameters is also highlighted.

کلیدواژه‌ها English

Digital Elevation Model
Forest
Handheld Laser Scanner
Octree
Segmentation
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  • تاریخ دریافت 08 مهر 1402
  • تاریخ بازنگری 07 آبان 1402
  • تاریخ پذیرش 05 آذر 1402