Retrieving Forest Canopy Height From ICESat-2 Data by an Improved DRAGANN Filtering Method and Canopy Top Photons Classification

Gao, Shijuan; Li, Yi; Zhu, Jianjun; Fu, Haiqiang; Zhou, Cui

doi:10.1109/lgrs.2022.3183683

Cited by 4 publications

(5 citation statements)

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“…Based on these characteristics, a signal can be distinguished from the edge noise. Within a local window, noise photons may exist in the elevation percentile ranges of 0-25% and 75-100%, which were selected to define potential noise across each along-track segment [25]. Next, iteration through these potential noise points was conducted.…”

Section: Edge Searching and Surface Lines Constraintsmentioning

confidence: 99%

“…The type 1 error is used to indicate when noise photons are misclassified as signal photons, and the type 2 error indicator is used to indicate when signal photons are misclassified as noise photons. Within a local window, noise photons may exist in the elevation percentile ranges of 0-25% and 75-100%, which were selected to define potential noise across each along-track segment [25]. Next, iteration through these potential noise points was conducted.…”

Section: Real Predictmentioning

confidence: 99%

“…When it comes to search domain improvement, horizontal ellipse-based OPTICS was developed by Zhu et al [ 24 ], which demonstrated greater robustness than the optimized DBSCAN [ 24 ]. Gao et al [ 25 ] enhanced the DRAGANN algorithm with a multi-scale search radius for solving the residual noise caused by a fixed radius. In terms of parameter determination, Huang et al [ 26 ] and Zhang et al [ 27 ] improved the self-adaptability of density-based methods based on a genetic algorithm and a particle swarm algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…There are no unified tools and standards for preparing samples, making it difficult apply them widely. However, there is residual noise after single-level filtering algorithms [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ], especially noise adjacent to the signal in complex terrains.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we propose a density-based multilevel terrain-adaptive noise removal method (MTANR) with a coarse-to-fine denoising strategy to address the issue. It is challenging to remove the noise adjacent to the signal and the removal requires a nearly noise-free environment; therefore, we categorized noise into three types [ 25 , 30 ]: cloud-caused high-altitude distant noise, sparse noise in medium distance, and edge noise adjacent to signal, as shown in Figure 1 . Based on the classification of noise photons, the denoising process was divided into three steps: (1) Distant noise and part of sparse noise were removed using a histogram-based successive method which gradually tightened conditions as a coarse denoising process.…”

Section: Introductionmentioning

confidence: 99%

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A Density-Based Multilevel Terrain-Adaptive Noise Removal Method for ICESat-2 Photon-Counting Data

Wang,

Zhang,

Zhang

et al. 2023

Sensors

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The photon point clouds collected by the high-sensitivity single-photon detector on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are utilized in various applications. However, the discretely distributed noise among the signal photons greatly increases the difficulty of signal extraction, especially the edge noise adjacent to signals. To detect signal photons from vegetation coverage areas at different slopes, this paper proposes a density-based multilevel terrain-adaptive noise removal method (MTANR) that identifies noise in a coarse-to-fine strategy based on the distribution of noise photons and is evaluated with high-precision airborne LiDAR data. First, the histogram-based successive denoising method was used as a coarse denoising process to remove distant noise and part of the sparse noise, thereby increasing the fault tolerance of the subsequent steps. Second, a rotatable ellipse that adaptively corrects the direction and shape based on the slope was utilized to search for the optimal filtering direction (OFD). Based on the direction, sparse noise removal was accomplished robustly using the Otsu’s method in conjunction with the ordering points to identify the clustering structure (OPTICS) and provide a nearly noise-free environment for edge searching. Finally, the edge noise was removed by near-ground edge searching, and the signal photons were better preserved by the surface lines. The proposed MTANR was validated in four typical experimental areas: two in Baishan, China, and two in Taranaki, New Zealand. A comparison was made with three other representative methods, namely differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN), used in ATL08 products, local distance statistics (LDS), and horizontal ellipse-based OPTICS. The results demonstrated that the values of the F1 score for the signal photon identification achieved by the proposed MTANR were 0.9762, 0.9857, 0.9839, and 0.9534, respectively, which were higher than those of the other methods mentioned above. In addition, the qualitative and quantitative results demonstrated that MTANR outperformed in scenes with steep slopes, abrupt terrain changes, and uneven vegetation coverage.

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Section: Edge Searching and Surface Lines Constraintsmentioning

confidence: 99%

Section: Real Predictmentioning

confidence: 99%