Abstract

ABSTRACT: Full-waveform (FWF) lidar system provides both geometric and waveform properties from the entire returned signals for analysis. As it provides more information than the conventional multi echo lidar, the waveform lidar plays an important role in land cover classification as well as object reconstruction. Nowadays, object-based image analysis (OBIA) has been widely applied in multispectral images. The idea of OBIA has been extended to object-based lidar points analysis. The objective of this research is to develop a procedure for object-based lidar points classification using waveform lidar data in a complex scene. There are two main steps in the proposed scheme: (1) point-based segmentation, and (2) object-based classification. Point-based segmentation uses a Euclidean clustering technique and points ’ attributes to merge the neighboring points with similar attributes. After segmentation, an object-based classification rather than point-based classification is performed. Each separated regions after segmentation is a candidate object for classification. An unsupervised Fuzzy c-mean classifier considering the characteristics of curvature, height, echo ratio, echo width, backscattering coefficient and shape information is performed to separate different land covers. The test data is acquired by Rigel Q680i and located in Tainan, Taiwan. The point density is 10 pt/m^2. The experimental result indicates that the proposed method may separate multilayer objects such as tree, building, vehicle, road, and ground. The overall accuracy reached 89 % for waveform features.