Multiscale Fusion Signal Extraction for Spaceborne Photon-Counting Laser Altimeter in Complex and Low Signal-to-Noise Ratio Scenarios

Abstract

Extracting signal photons from noisy raw data is one of the critical processes for the new generation of spaceborne photon-counting laser altimeter. Affected by vast noise photon-counting events, the extraction of weak signal events still faces challenges in complex scenarios with low signal-to-noise ratio (SNR). Aiming to improve the extraction ability of signal photon events in these scenarios, a multiscale fusion signal extraction method was proposed, characterized by combining global spatial correlation constraint with optimized local spatial correlation constraint. The local constraint is implemented based on a density-based spatial clustering of applications with noise (DBSCAN) clustering method with adaptive parameter estimation, which is used to extract possible signal photons. A subsequent global constraint based on the spatial correlation of the terrain profiles is designed to remove the pseudo-signal photons clustered in the local constraints’ step. The global constraint is implemented based on a cost function, which is used to quantify different candidate paths. Our method was verified based on the actual Ice, Cloud, and land Elevation satellite-(ICESat2) data containing vegetation, mountains, and residential areas. The experimental results show that compared with the ICESat-2 extraction method, our method can significantly improve the precision and recall rate of signal photon events from the low SNR photon-counting data.