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Waveform decomposition is a common step for exploitation of full-waveform lidar data. Much effort has been focused on designing algorithms based on the assumption that the returned waveforms follow a Gaussian mixture model where each component is a Gaussian. However, many real examples show that the waveform components can be neither Gaussian nor symmetric even when the emitted signal is Gaussian...
Waveform decomposition is an important step in full-waveform LiDAR remote sensing. Under the Gaussian Mixture Model, the conventional parametric classification algorithm of Expectation-Maximization (EM) is among the most widely applied ones to decompose the waveforms. This paper introduces nonparametric classification methods, such as K-means and mean-shift to decompose the LiDAR waveforms. The experiments...
Waveform decomposition is a necessary step for the exploitation of full waveform LiDAR data. Much effort has been focused on designing algorithms to decompose the waveform into a fixed number of components. However, the determination of the appropriate number of components in a waveform, though crucial, is rarely studied. This paper introduces an order identification method, Minimum Description Length...
Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings...
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