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Research on Airborne LIDAR Waveform Data Processing and Classifying

Author: XuGuangCai
Tutor: LiMingYangï¼›PangYong
School: Nanjing Forestry University
Course: Forest Management
Keywords: LIDAR full-waveform data Gaussian decomposition data correction data classification
CLC: P237
Type: Master's thesis
Year: 2010
Downloads: 199
Quote: 2
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LIDAR (light and range detecting) also referred to as airborne laser scanning (ALS) or laser radar, is an effective active remote sensing technique for acquiring 3D data of the nature environment. With the development of LIDAR system and storage technology, more and more airborne LIDAR systems have the ability of recording the whole waveform, which promote the application of this technology in the industry. Full-Waveform (FW) Lidar digitize and record the entire backscattered signal of each emitted pulse, which can give more control to the end users in the raw data managing and interpretation process comparing with the discrete return Lidar data. In this paper, we review some studies of FW Lidar, the history of FW Lidar systems and waveform data acquisition, data features and the need for further data processing firstly. FW Lidar systems have already proved a great potentialities and advantage in forest related applications. The processing of waveform data can gather more forestry characteristic information,which is important to forest structure extraction, vegetation type and tree species classification study. Afterwards, the whole Gaussian decomposition algorism of non-linear least-squares method, including FW data reading, removal of noise, smoothing the data, fitting and outputting the data, is concluded basing on the large number of experiments. Then real and simulated data are adopted to verify the accuracy of the Gaussian decomposition result. The IDL language is used to achieve the whole algorithmic process. Thirdly, correction of Gaussian decomposition result of FW data is finished by analyzing the LIDAR equation. At last, the calibration data of the study area is classified by two methods. Basing on the classified study area, the digital elevation model (DEM) and canopy height model (CHM) are generated. The end of this article summarizes the results, methods, problems, prospects and the future work.

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CLC: > Astronomy,Earth Sciences > Surveying and Mapping > Photogrammetry and Surveying, Mapping and Remote Sensing > Surveying, Mapping and Remote Sensing technology
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