A Density-Based Adaptive Ground and Canopy Detecting Method for ICESat-2 Photon-Counting Data

Xie, Huan; Ye, Dan; Xu, Qi; Sun, Yuan Tao; Huang, Peiqi; Tong, Xiaohua; Guo, Yalei; Liu, Xiaoshuai; Liu, Shijie

doi:10.1109/tgrs.2022.3176982

Cited by 20 publications

(15 citation statements)

References 23 publications

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“…For another, ICESat-2 has consistent pulse frequencies and sampling rates over surfaces with different slopes. For the same distance along the track, there are fewer returning photons in steep terrain than in flat areas with the slopes increasing [ 47 , 52 ]. It is necessary to expand the search domain to contain more photons for canopy detection.…”

Section: Experiments Results and Analysismentioning

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: Experiments Results and Analysismentioning

confidence: 99%

“…When the density on one side is three times greater than the other, it is considered an excessive difference in neighboring densities. In such cases, the optimal filtering direction is considered false [ 47 ].…”

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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“…We evaluated the ability of the algorithm to remove noise photons using single-category indicators and an overall indicator. For the single-category indicators, precision (P), recall (R), and F-measure (F) were used [23]. These indicators were calculated as follows:…”

Section: E Performance Evaluation Methodsmentioning

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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“…(1) Framing potential ground photons. As the land topography was more complex and variable than that of water surfaces, each land segment was reduced to sub-segments with a length τ c of 10 m. In mountain areas, the elevations of ground photons were typically located at 0-15% of the elevation range of all land signal photons [40]. In this study, the land photon whose elevation was within 0-15% of the signal elevation range in each sub-segment was considered to be a potential ground photon.…”

Section: Signal Photon Classificationmentioning

confidence: 99%

“…After determining the ground photons, the remaining signal photons were classified as ground covering photons in land segments. To further remove possible noise, the classified ground, ground covering, and water surface signal photons were filtered using the DBSCAN algorithm [37,40]. Figure 8 illustrates the final signal photons with their corresponding classification labels.…”

Section: Signal Photon Classificationmentioning

confidence: 99%

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

Song,

Ma,

Zhou

et al. 2024

Remote Sensing

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The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data.

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A Density-Based Adaptive Ground and Canopy Detecting Method for ICESat-2 Photon-Counting Data

Cited by 20 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 Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

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