Automatic land-sea classification in a nearshore environment using satellite-based photon-counting LiDAR data.

Abstract

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can measure the global surface with unprecedented resolution. Accurate classification of land and sea data is the prerequisite for generating high-quality data products. Current land-sea classification methods rely on assisted data or manual participation, and the automation degree cannot meet the needs of massive data processing. Therefore, using the land-sea difference of photon-counting LiDAR data, an index called normalized photon rate-elevation ratio (NPRER) is designed. Inspired by this, an automatic land-sea classification method is proposed, and the results are obtained through preliminary classification, reclassification, and post-processing enhancement. The results in Cook Inlet, Alaska, show that NPRER can measure the probability of sea appearance in the nearshore environment. At the same time, the automatic classification method can achieve an overall accuracy of 97.98%. The changes in the coastal type, data collection time, and classification feature sets have little influence on this method. Therefore, the method provides a reliable technical scheme for improving the automation of land-sea classification of satellite-based photon-counting LiDAR data.