A Two-Stage Nearshore Seafloor ICESat-2 Photon Data Filtering Method Considering the Spatial Relationship
“Ice, Cloud, and Land Elevation Satellite-2” (ICESat-2) produces photon-point clouds that can be used to obtain nearshore bathymetric data through density-based filtering methods. However, most traditional methods simplified the variable spatial density distribution of a photon to a linear relationship with water depth, causing a limited extraction effect. To address this limitation, we propose a two-stage filtering method that considers spatial relationships. Stage one constructs the adaptive photon density threshold by mapping a nonlinear relationship between the water depth and photon density to obtain initial signal photons. Stage two adopts a seed-point expanding method to fill gaps in initial signal photons to obtain continuous signal photons that more fully reflect seabed topography. The proposed method is applied to ICESat-2 data from Oahu Island and compared with three other density-based filtering methods: AVEBM (Adaptive Variable Ellipse filtering Bathymetric Method), Bimodal Gaussian fitting, and Quadtree Isolation. Our method (F-measure, F = 0.803) outperforms other methods (F = 0.745, 0.598, and 0.454, respectively). The accuracy of bathymetric data gained from seabed photons filtered using our method can achieve 0.615 m (Mean Absolute Error) and 0.716 m (Root Mean Squared Error). We demonstrate the effectiveness of incorporating photon spatial relationships to enhance the filtering of seabed signal photons.
Authors
- Xuying Wang (ORCID: https://orcid.org/0000-0001-7669-7658)
- Jianmai Shi (ORCID: https://orcid.org/0000-0003-2017-4019)
- Longjiao Zuo
- Qian Sun (ORCID: https://orcid.org/0000-0003-4718-5682)
- Yunsheng Zhang (ORCID: https://orcid.org/0000-0002-2779-2015)
Institutions
- Central South University (CN)
- National University of Defense Technology (CN)
- PowerChina (China) (CN)
- Powerchina Huadong Engineering Corporation (China) (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2024-12-23
- DOI
- https://doi.org/10.3390/rs16244795
- Citations
- 1
- Primary Topic
- Remote Sensing and LiDAR Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.21
Funders
- National Aeronautics and Space Administration
- China Railway
- National Natural Science Foundation of China