A Combined Deconvolution and Gaussian Decomposition Approach for Overlapped Peak Position Extraction From Large-Footprint Satellite Laser Altimeter Waveforms

In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this article, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) The deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) The combined approach can significantly improve the peak detection rate; the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind–Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind–Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.