Abstract
Abstract. In this paper, two different point cloud classification approaches were applied based on the full-waveform LiDAR data. At the beginning, based on the full-waveform LiDAR data, we decomposed the backscattered pulse waveform and abstracted each component in the waveform after the pre-processing of noise detection and waveform smoothing. And by the time flag of each component acquired in the decomposition procedure we calculated the three dimension coordination of the component. Then the components’ waveform properties, including amplitude, width and cross-section, were uniformed respectively and formed the Amplitude/Width/Section space. Then two different approaches were applied to classify the points. First, we selected certain targets and trained the parameters, after that, by the supervised classification way we segmented the study area point. On the other hand, we apply the IHSL colour transform to the above space to find a new space, RGB colour space, which has a uniform distinguishability among the parameters and contains the whole information of each component in Amplitude/Width/Section space. Then the fuzzy C-means algorithm is applied to the derived RGB space to complete the LiDAR point classification procedure. By comparing the two different segmentation results, which may of substantial importance for further targets detection and identification, a brief discussion and conclusion were brought out for further research and study.
Highlights
Airborne Laser Scanning (ALS) is an active remote sensing technique providing direct range measurements between laser scanner and objects, has witnessed an alternative source for acquisition of ranging data in last decade
Two different point cloud classification approaches were applied based on the full-waveform LiDAR data
By comparing the two different segmentation results, which may of substantial importance for further targets detection and identification, a brief discussion and conclusion were brought out for further research and study
Summary
Airborne Laser Scanning (ALS) is an active remote sensing technique providing direct range measurements between laser scanner and objects, has witnessed an alternative source for acquisition of ranging data in last decade. Depending on the geometry of illuminated surfaces, several backscattered echoes can be recorded for a single pulse emission. This showed the potential of multi-echo LiDAR data for urban area analysis and building extraction (Frueh et al, 2005). Since 2004, new commercial ALS systems called full-waveform (FW) LiDAR have emerged with the ability to record the complete waveform of the backscattered 1D-signal. Each echo in this signal corresponds to an encountered object. Two different point cloud classification approaches were applied based on the full-waveform LiDAR data. By comparing the two different segmentation results, which may of substantial importance for further targets detection and identification, a brief discussion and conclusion were brought out for further research and study
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