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A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data.
Longyu Wang1, Xuqing Zhang1, Ying Zhang1
1College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China.
Sensors (Basel, Switzerland)
|December 23, 2023
Summary
A new method, MTANR, effectively removes noise from ICESat-2 photon data, improving signal extraction for vegetation analysis. This terrain-adaptive approach enhances accuracy, especially on challenging slopes and varied terrain.
Area of Science:
- Geospatial analysis
- Remote sensing technology
- Photon point cloud processing
Background:
- ICESat-2 photon point clouds are crucial for applications but contain noise that hinders signal extraction.
- Edge noise near signals presents a significant challenge in data processing.
- Accurate signal detection is vital for understanding vegetation coverage and terrain characteristics.
Purpose of the Study:
- To propose a novel density-based multilevel terrain-adaptive noise removal method (MTANR) for ICESat-2 data.
- To enhance the accuracy of signal photon detection, particularly in vegetated areas with varying slopes.
- To develop a robust noise removal strategy that preserves signal photons effectively.
Main Methods:
- Implemented a coarse-to-fine noise identification strategy based on photon distribution.
- Utilized histogram-based successive denoising for initial noise reduction.
- Employed a rotatable ellipse and Otsu's method with OPTICS for adaptive sparse and edge noise removal.
- Validated the method using high-precision airborne LiDAR data.
Main Results:
- MTANR achieved high F1 scores (0.9534–0.9857) in signal photon identification across four test areas.
- Outperformed existing methods like DRAGANN, LDS, and horizontal ellipse-based OPTICS.
- Demonstrated superior performance in complex scenarios, including steep slopes, abrupt terrain changes, and uneven vegetation.
Conclusions:
- The MTANR method provides a significant advancement in processing ICESat-2 photon point clouds.
- It effectively removes various noise types while preserving crucial signal data.
- MTANR offers a robust solution for accurate vegetation and terrain analysis from satellite data.
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