Toward An Advanced Method for Full-Waveform Hyperspectral LiDAR Data Processing

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; p. 1
• Main Authors: Bai, Jie, Niu, Zheng, Bi, Kaiyi, Yang, Xuebo, Huang, Yanru, Fu, Yuwen, Wu, Mingquan, Wang, Li
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Components; Data analysis; Data processing; Datasets; Detection; Echoes; FWHM; Gaussian decomposition; Heterogeneity; Hyperspectral imaging; Laser radar; Laser theory; Lidar; multiple natural targets; Optimization; Parameters; range resolution; Rclonte-M; Reflection; Reflectivity; Simulation; waveform processing; Waveforms; Wavelength; Wavelength measurement; Wavelengths
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3382481

Full-waveform hyperspectral LiDAR (HSL) generates comprehensive hyperspectral waveforms for scenes to reveal the shape and spectral heterogeneity of multiple natural targets. Nevertheless, current waveform processing methods are primarily designed for single-wavelength LiDAR systems, resulting in a shortage of methods tailored for full-waveform HSL data processing and in a restriction to further quantitative applications for HSL. This study is designed to extract targets' physical and spectral characteristics by integrating spectral-dimension features into the HSL waveform processing. The core idea of the method involves a rigorous processing technique consisting of parameter initialization, parameter optimization, and re-optimization over calculating the median (M) after ranking central locations of natural target echoes (Rclonte). The medians in the re-optimization step serve as the reference parameter sets for supplementing the hidden or weak components at some wavelengths for HSL. Two groups of datasets, the simulated and measured datasets, were utilized to evaluate the component detection ability of the proposed Rclonte-M method. The results suggest that the Rclonte-M method demonstrates excellent component detection performance on both simulated and measured data, outperforming the multispectral waveform decomposition (MSWD) method. The HSL system designed by us owns an overall ranging error of about 7 cm for adjacent components, with the relative neighbor distance error (RNDE) limited to 0.160. Besides, spectra retrieval results from HSL easily distinguish the natural targets along the laser path. This study enriches the full-waveform HSL data processing algorithm library and could be considered in other full-waveform HSL systems and the simulated airborne or space-borne HSL waveforms. Codes are freely available on https://github.com/Jie-Bai/Rclonte-M-TGRS.