ارائة رویکردی خودکار برای تشخیص نقاط پرت در ابر نقاط لیدار به‌کمک SVM-CRF و نمودار جعبه‌ای

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد علوم تصمیم و مهندسی دانش، دانشگاه خوارزمی، تهران

2 دانشیار گروه مدیریت عملیات و فنّاوری اطلاعات، دانشگاه خوارزمی، تهران

3 استادیار مرکز مطالعات سنجش از دور و GIS، دانشگاه شهید بهشتی، تهران

چکیده

مجموعة داده‌های ابر نقاط لیدار و مدل‌های سه‌بعدی (3-D) در استخراج عوارض شهری، مدیریت جنگل‌داری، شهری و گردشگری، رباتیک، تولید بازی‌های رایانه‌ای و موارد دیگر کاربرد گسترده‌ای دارد. از سویی، وجود نقاط پرت در ابر نقاط لیدار اجتناب‌ناپذیر است؛ بنابراین تشخیص نقاط پرت و حذف آن‌ از ابر نقاط لیدار به‌منزلة گامی ضروری در پردازش ابر نقاط لیدار شناخته شده است. طی دهه‌های گذشته، چندین تکنیک تشخیص نقاط پرت در منابع این موضوع معرفی شده است اما بیشتر آنها از نظر زمانی هزینه‌برند و به نیروی متخصص انسانی نیاز دارند. به‌منظور کاهش این محدودیت‌ها، این مقاله رویکرد خودکار جدیدی برای تشخیص نقاط پرت، با استفاده از تکنیک میدان تصادفی شرطی برپایة ماشین بردار پشتیبان (SVM-CRF) و روش نمودار جعبه‌ای، معرفی کرده است. رویکرد نمودار جعبه‌ای بردار انرژی خروجی SVM-CRF را برای تشخیص نقاط پرت تجزیه و تحلیل می‌کند. این روش‌ به‌کمک مجموعه دادة محک ISPRS که برای مجموعه دادة وهینگن، با هدف طبقه‌بندی سه‌بعدی و بازسازی سه‌بعدی ساختمان ایجاد شده بود، ارزیابی شد. به‌منظور ارزیابی این روش، ابتدا نقاط پرتی به‌صورت دستی به مجموعه داده افزوده شد؛ با این تمرکز که این نقاط جزء نقاط پرت چسبیده به اشیا باشند. سپس مراحل تحقیق برای ارزیابی توانایی روش پیشنهادی در تشخیص نقاط پرت انجام شد. نتایج این تحقیق عملکرد مدل پیشنهادی را با دقت کلی 62% نشان داد. اگرچه الگوریتم RANSAC عملکردی بهتر از مدل پیشنهادی دارد، تکنیک زمان‌بر و پرهزینه‌تری در مقایسه با تکنیک تشخیص نقاط پرت پیشنهادی است.

کلیدواژه‌ها

عنوان مقاله [English]

SVM-CRF Method and Box Plot Technique for Outlier Detection of Lidar Point Cloud

نویسندگان [English]

  • Farzaneh Aghighi 1
  • Omid Mahdi Ebadati E. 2
  • Hossein Aghighi 3
1 Sc. in Knowledge Engineering and Decision Science, Kharazmi University, Tehran
2 Associate Prof., Dept. Operation Management & Information Technology, Kharazmi University, Tehran
3 Assistant Prof., Research Center of Remote Sensing and GIS, Shahid Beheshti University, Tehran
چکیده [English]

Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them from lidar point cloud data have been known as necessary steps in lidar point cloud processing. Over the past decade, several outlier detection techniques have been introduced in the literature; however, most of them are time-consuming, expensive, and computationally complicated. For overcoming these limitations, this article introduces a new automatic approach for outlier detection using a support vector machine-based conditional random field (SVM-CRF) technique and box plots methods. In this approach, a box plot analyzes the output energyvector of SVM-CRF to recognize outliers. The methods were evaluated using ISPRS benchmark datasets of Vaihingen provided in order to urban classification and 3D building reconstruction. To evaluate this method, first of all, outliers, that are almost closed to objects, were added to the data set manually. Then the research steps were done to evaluate the proposed method's ability for detecting outliers. The evaluation of this research showed an overall accuracy of 62% as the performance of the proposed model. Although the RANSAC algorithm has better performanc, it is a more costly and time-consuming technique than the proposed outlier detection technique.

کلیدواژه‌ها [English]

  • Lidar Point Cloud
  • Outlier Detection
  • SVM-CRF
  • Box Plot

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