An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

Yang, Panfeng; Fu, Haiqiang; Zhu, Jianjun; Li, Yi; Wang, Changcheng

doi:10.1109/lgrs.2021.3124612

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Given that for a photon there may be multiple RDs for it, the minimum RD is set as the optimal RD for it. After computing the optimal RDs for all photons, a threshold was obtained using Otsu’s method [ 24 , 48 , 49 , 50 ]. Based on the sparse distribution of noise photons, we identified photons with RDs greater than the threshold as noise.…”

Section: Methodsmentioning

confidence: 99%

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%

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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“…However, the signal photon density at the water surface is much denser than at the water bottom due to the gradual laser energy propagation inside the water. The conventional methods often fail to fully extract the signal photons from the water bottom, resulting in a lack of topographical information [24][25][26]. Experimental area D is located near Sanostee, within the state of New Mexico.…”

Section: Experimental Area and Datamentioning

confidence: 99%

“…Among them, the most used one is Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [20]. In order to include more signal photons in the search neighborhood, the circular neighborhood is commonly replaced by an elliptical neighborhood when the DBSCAN algorithm is applied [21][22][23][24][25][26]. Chen et al [21] proposed an adaptive variational ellipsoidal filtering bathymetry method which processes histogram statistics in the depth direction to separate surface photons from bottom ones, and adaptive variable ellipsoid filters allow the method to extract more signal photons at deeper regions.…”

Section: Introductionmentioning

confidence: 99%

“…Nan et al [23] proposed a method that combines local denoising with global denoising to effectively eliminate outliers, which improves the denoising accuracy by means of secondary denoising. Yang et al [24] proposed backward elliptic distance (BED) to solve the signal photons fluctuate problem by rotating the elliptic domain, thus minimizing the points in the neighborhood. This method can obtain good results in mountainous areas.…”

Section: Introductionmentioning

confidence: 99%

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A Novel ICESat-2 Signal Photon Extraction Method Based on Convolutional Neural Network

Qin,

Song,

Zou

et al. 2024

Remote Sensing

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When it comes to the application of the photon data gathered by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), accurately removing noise is crucial. In particular, conventional denoising algorithms based on local density are susceptible to missing some signal photons when there is uneven signal density distribution, as well as being susceptible to misclassifying noise photons near the signal photons; the application of deep learning remains untapped in this domain as well. To solve these problems, a method for extracting signal photons based on a GoogLeNet model fused with a Convolutional Block Attention Module (CBAM) is proposed. The network model can make good use of the distribution information of each photon’s neighborhood, and simultaneously extract signal photons with different photon densities to avoid misclassification of noise photons. The CBAM enhances the network to focus more on learning the crucial features and improves its discriminative ability. In the experiments, simulation photon data in different signal-to-noise ratios (SNR) levels are utilized to demonstrate the superiority and accuracy of the proposed method. The results from signal extraction using the proposed method in four experimental areas outperform the conventional methods, with overall accuracy exceeding 98%. In the real validation experiments, reference data from four experimental areas are collected, and the elevation of signal photons extracted by the proposed method is proven to be consistent with the reference elevation, with R2 exceeding 0.87. Both simulation and real validation experiments demonstrate that the proposed method is effective and accurate for extracting signal photons.

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“…The density feature mainly uses the significant difference in density between the signal photons and noise photons [18]- [20]. This method can be used to divide the photon data area and then sets a threshold to remove noise by counting the number of photons in different partition areas [21]- [24]. The randomness of noisy photons in photon data has been considered in previous studies.…”

mentioning

confidence: 99%

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

Chang,

Jiang,

Lin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with an advanced topographic laser altimeter system (ATLAS) that obtains small spots and highdensity photon data. ATLAS has the potential for high-precision detection of global surfaces and helps calculate global carbon sinks. One important carbon sink includes forests and making it critical to precisely measure their carbon storage and obtain both ground and top-of-canopy (TOC) levels of vegetation. Here, we explored a precise and highly reliable ground and TOC detection method based on ICESat-2 data to accurately remove noise and classify photons. This method can be adapted to different scenes and topographies. The first step included removal of pronounced dispersion noise and pseudo-signal photon aggregation noise. We introduced two algorithms for coarse noise removal-an adaptive threshold ellipsoid filter algorithm and an outlier photon cluster removal algorithm based on the photon group distance. Then, the local outlier factor (LOF) algorithm was modified to remove the noise of near-signal photons to obtain the result of fine denoising. Finally, the modified local direction center algorithm was combined with the uniaxial inverse distance weighted statistics to distinguish the ground from the TOC. Across varied scenes and terrains, the proposed denoising method demonstrated an overall accuracy exceeding 0.94. Additionally, the root mean square error of the ground and TOC obtained via the classification method was under 1.4 m and 3.2 m, respectively. These findings highlight that this method has a high level of robustness in effectively detecting both the ground and TOC.

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An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

Cited by 12 publications

References 23 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

A Novel ICESat-2 Signal Photon Extraction Method Based on Convolutional Neural Network

A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

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