LiDAR data filtering and DTM generation using empirical mode decomposition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, cilt.10, sa.1, ss.360-371, 2017 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 10 Sayı: 1
- Basım Tarihi: 2017
- Doi Numarası: 10.1109/jstars.2016.2543464
- Dergi Adı: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.360-371
- Anahtar Kelimeler: decomposition (EMD), Digital surface model (DSM), digital terrain model (DTM), empirical mode, intrinsic mode functions (IMFs), LiDAR, object detection
- İstanbul Medipol Üniversitesi Adresli: Hayır
Özet
LiDAR technology is advancing. As a result, researchers can benefit from high-resolution height data from Earth's surface. Digital terrain model (DTM) generation and point classification (filtering) are two important problems for LiDAR data. These are connected problems since solving one helps solving the other. Manual classification of LiDAR point data could be time consuming and prone to errors. Hence, it would not be feasible. Therefore, researchers proposed several methods to solve DTM generation and point classification problems. Although these methods work fairly well in most cases, they may not be effective for all scenarios. To contribute in this research topic, a novel method based on two-dimensional (2-D) empirical mode decomposition (EMD) is proposed in this study. Local, nonlinear, and nonstationary characteristics of EMD allow better DTM generation. The proposed method is tested on two publicly available LiDAR dataset, and promising results are obtained. Besides, the proposed method is compared with other methods in the literature. Comparison results indicate that the proposed method has certain advantages in terms of performance.