Compression strategies for LiDAR waveform cube

Abstract

Full-waveform LiDAR data (FWD) provide a wealth of information about the shape and materials of the surveyed areas. Unlike discrete data that retains only a few strong returns, FWD generally keeps the whole signal, at all times, regardless of the signal intensity. Hence, FWD will have an increasingly well-deserved role in mapping and beyond, in the much desired classification in the raw data format. Full-waveform systems currently perform only the recording of the waveform data at the acquisition stage; the return extraction is mostly deferred to post-processing. Although the full waveform preserves most of the details of the real data, it presents a serious practical challenge for a wide use: much larger datasets compared to those from the classical discrete return systems. Atop the need for more storage space, the acquisition speed of the FWD may also limit the pulse rate on most systems that cannot store data fast enough, and thus, reduces the perceived system performance. This work introduces a waveform cube model to compress waveforms in selected subsets of the cube, aimed at achieving decreased storage while maintaining the maximum pulse rate of FWD systems. In our experiments, the waveform cube is compressed using classical methods for 2D imagery that are further tested to assess the feasibility of the proposed solution. The spatial distribution of airborne waveform data is irregular; however, the manner of the FWD acquisition allows the organization of the waveforms in a regular 3D structure similar to familiar multi-component imagery, as those of hyper-spectral cubes or 3D volumetric tomography scans.This study presents the performance analysis of several lossy compression methods applied to the LiDAR waveform cube, including JPEG-1, JPEG-2000, and PCA-based techniques. Wide ranges of tests performed on real airborne datasets have demonstrated the benefits of the JPEG-2000 Standard where high compression rates incur fairly small data degradation. In addition, the JPEG-2000 Standard-compliant compression implementation can be fast and, thus, used in real-time systems, as compressed data sequences can be formed progressively during the waveform data collection. We conclude from our experiments that 2D image compression strategies are feasible and efficient approaches, thus they might be applied during the acquisition of the FWD sensors.