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Using airborne full-waveform LiDAR metrics derived by 3-D tree segmentation, this study estimated single tree's diameter at breast height (DBH) and stem volume (STV). Four regression models were used, including multilinear regression and three up-to-date regression models (i.e., least square boosting trees regression, random forest, and $\varepsilon$-support vector regression) from the machine learning...
Chronic diseases are gradually becoming the principal factors of harm to people's health. Fortunately, the development of e-health provides a novel thought for chronic disease prevention and treatment. This paper focuses on the research of cardiovascular disease (CVDs) prevention and early warning techniques using e-health and data mining. In this paper, we will use weighted associative classification...
Airborne LiDAR (ALS) data are characterized by involving not only rich spatial but also temporal information. It is possible to extract moving vehicles with motion artifacts from single-pass airborne LiDAR data, based on which the velocity of vehicles can be derived. In this paper, a series of methods for velocity estimation of moving vehicles using airborne LiDAR data is presented and evaluated....
a new semi-supervised classification method is proposed by combining airborne LiDAR (Light Detection And Ranging) data with registered aerial images. Firstly, the algorithm filtered LiDAR data into ground points and non-ground points that were further partitioned into small planar regions based on local attribute estimation. Then these planar regions will be used as initial classes to obtain initial...
As a new kind of remote sensing sensor, airborne LiDAR (Light detection and Ranging) has gained much attention by photogrammetry and remote sensing community. This paper focuses on the matching and strip adjustment of airborne LiDAR data. A multi-strip least squares matching (LSM) method was proposed, which adopts a combined feature called quasi-height including both the height and reflectance information...
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