Classification and change detection using point clouds

Abstract

The point cloud is a very powerful source for deriving 3D models which are widely applied in natural resource, environmental management, and urban domain. Point cloud classification and change detection are used in the context of Earth observation to monitor and assess the status and change of the natural and built environment. They have an essential role in providing and updating information in three dimensions compared to the provision of 2D information from traditional raster images. There are a number of sensors and platforms that acquire point clouds at different resolutions and spaces, in those, airborne laser scanning (ALS) and image matching (IM) are two main sources which allow to collect point clouds over large areas. The number of published research articles regarding to point cloud classification and change detection is increasing. Many studies uses ALS data on classification and change detection, but concentration on raster, and fewer publication on point clouds. In addition, image matching point cloud classification draw a less attention so far compared to ALS data. The objectives of this dissertation are focused on point cloud classification and change detection based on raster-based and point-based approaches to consider advantages they bring in different levels of details and types of datasets. This includes finding effective attributes for classifying and detecting changes, transferring attribute thresholds between different data sets and locations, and evaluating the benefit of machine learning in classification and change detection. The study questions range from measurement technology via feature derivation to processing methods are investigated and evaluated in four research articles. The presented studies and are published in peer-reviewed journals and a conference paper. Article I and II investigate the classification using (i) full-waveform airborne laser scanning, and (ii) an image matching point cloud based on simple decision tree and machine learning method. The presented approaches show high potential for classifying multiple objects over urban areas. Article III investigates the reduction of individual trees in forested area using traditional image differencing method. The presented method finds new features of the LiDAR point cloud, which are useful for detecting single object change in wooded areas. Finally, Article IV investigates the integration of detecting and classifying changes simultaneously for multi-objects change detection in urban area based on airborne laser scanning data. The presented studies prove, that the point cloud, either acquired by airborne laser scanning or by image matching, is an effective and practicable data source for accurate classification and change detection in large areas.