Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Abstract

The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data.