Hava LiDAR Verilerininin Basit Üçgen Düzensiz Ağ Algoritması ile Filtrelenmesi

Year 2024, Volume: 6 Issue: 3

Fırat Uray , Abdullah Varlık

Abstract

Son yıllarda havadan ve karadan lazer tarama sistemleri jeo-uzamsal bilgi elde etmek için giderek daha popüler hale gelmiştir. Yüksek kaliteli 3B nokta bulutları çok çeşitli uygulamalar için kullanılmakta olup sayısal arazi modelleri (SAM) bu ürünlerden birini temsil etmektedir. Light Detection and Ranging (LiDAR) verileri, planlama uygulamalarının temel unsurları olan sayısal yükseklik modellerinin (SYM) üretilmesinde yaygın olarak kullanılmaktadır. LiDAR nokta bulutlarından zemin dışı nesnelerin çıkarılması, DTM üretim iş akışının ana sorunudur. Üçgenleştirilmiş düzensiz ağ (DÜA) yoğunlaştırma, LiDAR filtreleme algoritmaları arasında klasik bir tekniktir. Bu çalışmada, klasik DÜA yoğunlaştırmadan türetilen basit DÜA yoğunlaştırma (sTIN) adlı basit bir filtreleme algoritması önerilmiştir. sTIN'in performansı, uyarlanabilir üçgenleştirilmiş düzensiz ağ, basit morfolojik filtre ve geliştirilmiş aşamalı DÜA yoğunlaştırma olmak üzere üç filtre ile test edilmiştir. Önerilen algoritmanın ortalama tip I hata oranı %5,6, ortalama tip II hata oranı %10,42 ve ortalama toplam hata oranı %8,2'dir. Ayrıca, algoritmaların güçlü ve zayıf yönleri, karesel ortalama hata (KOH) değerleri ve SAM'ların görsel analizleri açısından incelenmiştir.

Keywords

Uzaktan Algılama, Nokta Bulutu, LiDAR, Filtreleme, Sınıflandırma

Filtering Airborne LIDAR Data Using Simple Triangulation Irregular Network

Abstract

In recent years, airborne and terrestrial laser scanning systems have become increasingly popular for obtaining geospatial information. High-quality 3D point clouds are used for a wide range of applications, with digital terrain models (DTMs) representing one of these products. Light Detection and Ranging (LiDAR) data is widely used in producing digital elevation models (DEM), which are fundamental elements of planning applications. Removing non-ground objects from LiDAR point clouds is the main problem of the DTM production workflow. Triangulated irregular network (TIN) densification is a classical technique among LiDAR filtering algorithms. In this study, a simple filtering algorithm entitled simple TIN densification (sTIN) is proposed, which is derived from classic TIN densification. The performance of sTIN is tested with three filters, namely, the adaptive triangulated irregular network, the simple morphological filter and the improved progressive TIN densification. The proposed algorithm has an average type I error rate of 5.6%, an average type II error rate of 10.42% and an average total error rate of 8.2%. In addition, the strengths and weaknesses of the algorithms are examined with regards to the root-mean-square error (RMSE) values and visual analyses of DTMs.

Keywords

Remote Sensing, Point Cloud, LiDAR, Filtering, Classification

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