# Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Description and Experimental Area

#### 2.1.1. ICESat-2 Data

#### 2.1.2. Airborne LiDAR Data and Experimental Area

#### 2.2. Methods

#### 2.2.1. The DRAGANN Algorithm

#### 2.2.2. The RBF Algorithm

#### 2.2.3. The DBSCAN Algorithm

#### 2.2.4. Accuracy Verification

## 3. Results

#### 3.1. Analysis of the Effect of FVC on the Denoising Results of Three Algorithms

#### 3.2. Analysis of the Effect of Slope on the Denoising Results of Three Algorithms

#### 3.3. Analysis of the Effect of Observation Time on the Denoising Results of Three Algorithms

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Schematic representation of the distribution of the 6 laser beams and (virtual) sub-satellite points (7*) of the ICESat-2 satellite. The laser beams are divided into three groups along the orbital direction, each containing a strong and a weak beam with an energy ratio of 4:1, which is used to adapt to the reflective properties of different surface types in order to provide better elevation measurement data [27] (Reprinted with permission from Ref. [27]. 2018, Elsevier Inc).

**Figure 3.**Point-cloud denoising results for high FVC. (

**a**,

**b**) are the daytime and night-time denoising results of the DRAGANN algorithm, respectively. (

**c**,

**d**) are the daytime and night-time denoising results of the RBF algorithm, respectively. (

**e**,

**f**) are the daytime and night-time denoising results of the DBSCAN algorithm, respectively.

**Figure 4.**The comprehensive evaluation index F value of different slope grades. (

**a**) The F value of daytime DRAGANN algorithm, RBF algorithm and DBSCAN algorithm at different slope grades. (

**b**) The F value of night-time DRAGANN algorithm, RBF algorithm and DBSCAN algorithm at different slope grades.

**Figure 5.**Point−cloud denoising results for high slope. (

**a**,

**b**) are the daytime and night-time denoising results of the DRAGANN algorithm, respectively. (

**c**,

**d**) are the daytime and night-time denoising results of the RBF algorithm, respectively. (

**e**,

**f**) are the daytime and night-time denoising results of the DBSCAN algorithm, respectively.

**Figure 6.**Point-cloud denoising results under different time period conditions. (

**a**,

**b**) are the daytime and night-time denoising results of the DRAGANN algorithm, respectively. (

**c**,

**d**) are the daytime and night-time denoising results of the RBF algorithm, respectively. (

**e**,

**f**) are the daytime and night-time denoising results of the DBSCAN algorithm, respectively.

**Figure 7.**Threshold extraction results. (

**a**) Daytime threshold extraction results. (

**b**) Threshold extraction results when the FVC is 45% or more than 75% at night. (

**c**) Threshold extraction results at night when the FVC is 45~75%.

**Table 1.**Detailed information of the experimental data

^{1}. FVC was categorized into classes I, II, III, and IV, which varied from 0 to 20%, 20% to 45%, 45% to 75% and 75% to 100%, respectively, and terrain slope was categorized into classes I, II, III and IV, which varied from 0 to 5°, 5° to 15°, 15° to 25° and 25° to 60°, respectively.

Number | NEON Site Name | ICESat-2/ATL03 Time | FVC | Number | NEON Site Name | ICESat-2/ATL03 Time | Slope |
---|---|---|---|---|---|---|---|

Data1 | MOAB | 2022.07.13.Daytime | Ⅰ | Data9 | MOAB | 2022.07.13.Daytime | Ⅰ |

Data2 | MOAB | 2020.09.21.Night | Ⅰ | Data10 | MOAB | 2020.09.21.Night | Ⅰ |

Data3 | ONAQ | 2022.07.31.Daytime | Ⅱ | Data11 | MOAB | 2021.09.16.Daytime | Ⅱ |

Data4 | ONAQ | 2022.07.31.Night | Ⅱ | Data12 | MOAB | 2020.04.17.Night | Ⅱ |

Data5 | BART | 2020.07.03.Daytime | Ⅲ | Data13 | NIWO | 2021.08.27.Daytime | Ⅲ |

Data6 | BART | 2019.09.03.Night | Ⅲ | Data14 | NIWO | 2020.06.02.Night | Ⅲ |

Data7 | HARV | 2022.07.07.Daytime | Ⅳ | Data15 | NIWO | 2021.08.27.Daytime | Ⅳ |

Data8 | HARV | 2020.08.09.Night | Ⅳ | Data16 | NIWO | 2020.06.02.Night | Ⅳ |

^{1}To ensure the validity of the experiment, the slopes of Data1~Data8 are of the same classification, and the vegetation cover of Data9~Data16 are of the same classification.

Number | DRAGANN Algorithm | RBF Algorithm | DBSCAN Algorithm | ||||||
---|---|---|---|---|---|---|---|---|---|

R | P | F | R | P | F | R | P | F | |

Data1 | 1 | 0.795 | 0.886 | 0.999 | 0.930 | 0.963 | 1 | 0.730 | 0.844 |

Data2 | 1 | 0.832 | 0.908 | 1 | 0.934 | 0.966 | 0.998 | 0.840 | 0.952 |

Data3 | 1 | 0.799 | 0.888 | 1 | 0.921 | 0.959 | 1 | 0.726 | 0.841 |

Data4 | 1 | 0.859 | 0.924 | 1 | 0.925 | 0.961 | 1 | 0.859 | 0.924 |

Data5 | 0.944 | 0.890 | 0.916 | 0.942 | 0.897 | 0.919 | 0.941 | 0.887 | 0.913 |

Data6 | 1 | 0.901 | 0.948 | 0.998 | 0.903 | 0.948 | 0.998 | 0.912 | 0.953 |

Data7 | 0.928 | 0.824 | 0.873 | 0.976 | 0.872 | 0.921 | 0.925 | 0.798 | 0.857 |

Data8 | 0.982 | 0.874 | 0.925 | 0.998 | 0.857 | 0.922 | 0.897 | 0.887 | 0.892 |

Environmental Factors | DRAGANN Algorithm | RBF Algorithm | DBSCAN Algorithm | |||
---|---|---|---|---|---|---|

Daytime | Night | Daytime | Night | Daytime | Night | |

FVC | 0.024 | 0.021 | 0.016 | 0.015 | 0.044 | 0.039 |

Number | DRAGANN Algorithm | RBF Algorithm | DBSCAN Algorithm | ||||||
---|---|---|---|---|---|---|---|---|---|

R | P | F | R | P | F | R | P | F | |

Data9 | 1 | 0.795 | 0.886 | 0.999 | 0.930 | 0.963 | 1 | 0.730 | 0.844 |

Data10 | 1 | 0.832 | 0.908 | 1 | 0.934 | 0.966 | 0.998 | 0.840 | 0.912 |

Data11 | 1 | 0.783 | 0.878 | 0.978 | 0.912 | 0.944 | 1 | 0.723 | 0.839 |

Data12 | 1 | 0.826 | 0.905 | 0.988 | 0.909 | 0.947 | 1 | 0.830 | 0.907 |

Data13 | 1 | 0.757 | 0.862 | 0.961 | 0.861 | 0.908 | 1 | 0.715 | 0.834 |

Data14 | 1 | 0.813 | 0.897 | 0.981 | 0.850 | 0.911 | 1 | 0.821 | 0.902 |

Data15 | 1 | 0.751 | 0.858 | 0.953 | 0.809 | 0.875 | 1 | 0.704 | 0.826 |

Data16 | 1 | 0.807 | 0.893 | 0.959 | 0.811 | 0.879 | 1 | 0.817 | 0.899 |

Time | DRAGANN Algorithm | RBF Algorithm | DBSCAN Algorithm |
---|---|---|---|

Daytime | 0.880 | 0.927 | 0.850 |

Night | 0.914 | 0.933 | 0.930 |

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## Share and Cite

**MDPI and ACS Style**

Kui, M.; Xu, Y.; Wang, J.; Cheng, F.
Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data. *Remote Sens.* **2023**, *15*, 3884.
https://doi.org/10.3390/rs15153884

**AMA Style**

Kui M, Xu Y, Wang J, Cheng F.
Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data. *Remote Sensing*. 2023; 15(15):3884.
https://doi.org/10.3390/rs15153884

**Chicago/Turabian Style**

Kui, Mengyun, Yunna Xu, Jinliang Wang, and Feng Cheng.
2023. "Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data" *Remote Sensing* 15, no. 15: 3884.
https://doi.org/10.3390/rs15153884