The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022
 
30 May 2022
30 May 2022

AUTO-ADAPTIVE MULTI-LEVEL SEAFLOOR RECOGNITION AND LAND SEA CLASSIFICATION (AMSRLC) IN REEF-ISLAND ZONES USING ICESAT-2 LASER ALTIMETRY

Q. Xu1, H. Xie1,2, Y. Sun1, X. Liu1, Y. Guo1, P. Huang1, B. Li1, and X. Tong1 Q. Xu et al.
  • 1College of Surveying and Geo-Informatics, and Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai, 200092, China
  • 2Shanghai Institute of Intelligent Science and Technology, Shanghai, 200092, China

Keywords: ICESat-2, Seafloor Recognition, Photon Classification, Reef/island Zones, Confidence Evaluation

Abstract. The world’s first photon-counting laser altimetry satellite, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), which was launched in 2018, has proven to have a certain bathymetric capability, which provides a new means for the surveying of island and reef zones. However, how to accurately extract and separate land surface signal photons, sea surface signal photons, and seafloor signal photons in these areas has not yet been resolved. In this paper, we propose a validated auto-adaptive multi-level classification algorithm (AMSRLC), which can realize automatic recognition and classification of seafloor, sea surface, and land surface photons in island and reef zones. A overlapping histogram, a slope-adaptive search ellipse, and a water depth adaptive local signal-to-noise ratio are respectively used to extract flat sea surface signals, island and reef surface signals with slope changes, and seafloor signals that weaken with water depth. The overall classification indicators OA, AA, and Kappa reached 0.993, 0.973, and 0.987 respectively. The algorithm can effectively detect various signals with high detection accuracy.