Gaussian Decomposition of Full-waveform LiDAR Based on Grouping LM Algorithm. WANG Suyuan, MA Hongchao, WANG Jiedong, et al. Technology of Laser Large-footprint Multi-Objective Relative Distance Information Extraction and Verification. LAN Xiaoping, HUANG Genghua, WANG Haiwei, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 1999, 54(2-3): 64–67. Airborne Laser Scanning-Present Status and Future Expectations. The results show that the suggested algorithm is efficient, promising and can effectively decompose adjacent echo components, then will improve the accuracy in the next phase of data processing. To further demonstrate the advantages of the suggested method, waveform data measured by a full waveform LiDAR demonstration system and generated from simulation were both decomposed using the method. To this end, in this paper a novel method based on LM algorithm that takes into account both peak points and inflection points is adopted and it can extract the location, amplitude and FHWM of the echo components, proving it is a reliable and high accurate decomposition algorithm. Conventional decomposition methods detect echo components by peak points or using the threshold method, which may ignore some overlapping components and show low accuracy. However, the number and initial parameters of the echo components are difficult to set in waveform decomposition. Waveform decomposition is therefore the key to full waveform LiDAR data processing. Abstract: With the improvement of data storage capacity and data-processing capabilities, full waveform LiDAR develops rapidly and then from waveform data abundant information about the physical characteristics of the targets can be effectively retrieved through data processing.
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