A Novel Noise Filtering Evaluation Criterion of ICESat-2 Signal Photon Data in Forest Environments

Huang, Jiapeng; Xing, Yunqi; Shuai, Yanmin; Zhu, Huizhong

doi:10.1109/lgrs.2022.3163143

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…However, they are not accurate enough to serve as references for complex topographies such as hilly and mountainous terrains. Some existing approaches for quantitative denoising output assessment typically rely on the manual interpretation of Google Earth satellite images to construct validation datasets [ 39 , 40 ]. In this sense, these subjectively labeled references may be imprecise.…”

Section: Methodsmentioning

confidence: 99%

“…In this sense, these subjectively labeled references may be imprecise. For this reason, we employed the airborne point cloud with high density as the reference data to evaluate the signal extraction algorithm presented in this work [ 39 ], which provides an increasingly objective benchmark to quantify denoising accuracy. For supplementary validation, the digital terrain model (DTM) and digital surface model (DSM) with a horizontal resolution of 1 m from the airborne LiDAR data of approximately 10 pts/m 2 were used to serve as boundaries for the signal photons.…”

Section: Methodsmentioning

confidence: 99%

“…The airborne data in Baishan was collected in 2014, a few years ahead of ICESat 2’s acquisition time. So in this area, we set a 0.5 m buffer zone to mitigate the impact of changes over time, according to the research of Huang et al [ 39 ].…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

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 "DTM − 0.5 m" and "DSM + 0.5 m" are employed as the ground boundary and the top boundary of the canopy, respectively. The points within the boundary of both sides are signal photons, and the points outside the boundary are noise photons [26]. The evaluation indicators used in this study primarily include recall (R), precision (P), and comprehensive evaluation index value (F).…”

Section: Accuracy Evaluationmentioning

confidence: 99%

Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise

You,

Li,

Qin

et al. 2023

Remote Sensing

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which can achieve high-precision ground detection. However, due to its low pulse energy and high sensitivity, it is also affected by noise when obtaining data. Therefore, data denoising is critical to the subsequent processing and application. In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise. Firstly, the Random Sample Consensus (RANSAC) algorithm is used to roughly denoise the daytime photon cloud data with high background noise, and a signal point cloud buffer is established to remove most of the noise points. Subsequently, the horizontal continuity parameter is calculated based on the photon cloud data following the rough denoising process. Based on this parameter, the shape and size of the search domain of the results of the subsequent fine denoising algorithm are adaptively improved. Finally, three filtering directions (a horizontal direction, an intra-group unified direction, and an adaptive direction for each photon) are proposed, and a hybrid algorithm combining the Ordering Points to Identify the Clustering Structure (OPTICS) density clustering algorithm and the Relative Neighboring Relationship K-nearest neighbors-based noise removal (RNR−KNNB) algorithm is employed to accurately denoise the photon cloud data in the three filtering directions. Furthermore, the RANSAC algorithm based on a sliding overlap window is used to remove outliers for the weak beam fine denoising photon cloud data. The results indicate that, for the strong beams, the denoising accuracy of the multilevel filtering algorithm in the three filtering directions is comparable (Rs ≥ 0.96, F ≥ 0.67), and they are all better than that of the ATL08 algorithm (Rs/Rn/p/F = 0.85/0.67/0.52/0.65). For weak beams, the denoising accuracy of the multilevel filtering algorithm in the horizontal direction and the intra-group unified direction is similar (Rs = 0.92, F = 0.69), and it is superior to the denoising results in the adaptive direction of each photon and the ATL08 algorithm (Rs/Rn/p/F = 0.94/0.84/0.51/0.65, 0.88/0.87/0.55/0.67, respectively). For strong and weak beams, the p-value and F-value of the denoising results of multilevel filtering algorithms in three different filtering directions increase with the increase of SNR value. It is demonstrated that SNR is an important factor affecting the denoising results of algorithms. The multilevel filtering algorithm proposed in the study can effectively achieve precise denoising of daytime photon cloud data with high background noise, and the three different filtering directions have different effects on the denoising results of strong and weak beam photon cloud data. This can provide technical and methodological guidance for subsequent photon cloud data filtering processing.

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Icesat-2 Data Denoising and Forest Canopy Height Estimation Using Machine Learning

Kong,

Pang

2024

Preprint

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A Novel Noise Filtering Evaluation Criterion of ICESat-2 Signal Photon Data in Forest Environments

Cited by 3 publications

References 13 publications

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

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

Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise

Icesat-2 Data Denoising and Forest Canopy Height Estimation Using Machine Learning

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