# Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation

## Abstract

**:**

## 1. Introduction

## 2. Algorithm

_{i}is defined as

_{i}, y

_{i}, and z

_{i}are the x, y, and z coordinates of point i, respectively, and x

_{tgt}, y

_{tgt}, and z

_{tgt}are the x, y, and z coordinates of the target ground point, respectively (Figure 2). When calculating the slope, the original xy coordinates of the LLP are used. Others data are temporarily labeled as non-GP candidates. Step (5) adds more GPs. If non-GP candidates within the same window size as Step (3) are closer to the GP plane than a designated threshold (as indicated in Figure 2), they are labeled as GPs. Although some of the actual GPs are not extracted in Step (4), more are extracted at this stage.

## 3. Data Characteristics

## 4. Experiments

#### 4.1. Results

#### 4.2. Validation

#### 4.2.1. Validation Using ISPRS Benchmark Data

#### 4.2.2. Comparison with TerraScan Using Study Area Data

## 5. Discussion

#### 5.1. Qualitative and Quantitative Assessment

#### 5.2. Effect of Updating Slope Parameter

#### 5.3. Definition of Slope Angle

#### 5.4. Effect of Water Body Mask

#### 5.5. Computation Time and Limitations

## 6. Conclusions

## Acknowledgments

## References and Notes

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**Figure 2.**Searching for new GPs by using planes. A new GP is added when the distance between the point and plane calculated at the target ground point is within a threshold, and the horizontal distance between the point and target ground point is within another threshold, “Window size”.

**Figure 3.**Generation of DTM for Higashiyama: (

**a**) aerial photograph, (

**b**) original airborne LiDAR data, (

**c**) points extracted as planar surface areas, (

**d**) GPs after first iteration of first loop, (

**e**) GPs after iteration ended in first loop, (

**f**) GPs after iteration ended in second loop, (

**g**) final DTM with GPs, (

**h**) “Maximum slope” of final DTM, and (

**i**) comparison between results obtained by using the proposed algorithm and those obtained by using TerraScan. In (

**g**), black pixels denote GPs. In (h), black and white pixels denote 3.0° and 4.5° maximum slopes, respectively. In (i), black pixels denote GPs extracted by both the proposed algorithm and TerraScan, red pixels denote GPs extracted by using only the proposed algorithm, and blue pixels denote GPs extracted by using only TerraScan. Central latitude: 34°59′56′′N; Central longitude: 135°46′42′′E.

**Figure 4.**Generation of DTM for Nakagyo: (

**a**) aerial photograph, (

**b**) original airborne LiDAR data, (

**c**) final DTM with GPs, (

**d**) “Maximum slope” of the final DTM, and (

**e**) comparison between results obtained by using the proposed algorithm and those obtained by using TerraScan. Explanations for (d) and (e) are the same as those for Figure 3(h,i), respectively. In DTM images, bodies of water are shown in white. Central latitude: 34°59′56′′N; Central longitude: 135°45′′57′′E.

**Figure 5.**Effect of water body mask for Nakagyo dataset: (

**a**), (

**c**) final DTMs with GPs generated by proposed algorithm with water body mask, and (

**b**), (

**d**) final DTMs with GPs generated by proposed algorithm without water body mask.

Measurement date | June 2002 to February 2003 |

Measurer | Aero Asahi Corporation |

Density | Approx. 0.68 points/m^{2} |

(calculated as valid pixels divided by all pixels of the area) | 0.70 for Higashiyama, and 0.66 for Nakagyo |

Altitude | 900 to 1,000 m |

Horizontal accuracy | ±50 cm |

Vertical accuracy | ±15 cm |

Process | Parameter | Value Used |
---|---|---|

Step (1): River extraction | Window size for initial area detection | 7 m × 7 m |

Minimum area to accept water body | 100 m^{2} | |

Step (2): Finding locally lowest points (LLPs) | Window size | 60 m × 60 m |

Step (3): Planar surface calculation | Window size (used also in Steps (4) and (5)) | 5 m × 5 m |

Minimum data number to calculate | 6 points | |

Maximum root mean square of errors (RMSE) to accept plane | 0.1 m | |

Maximum distance to plane (used also in Step (5)) | 0.1 m | |

Minimum vertical component of planar normal | 0.9 | |

Step (4): GP determination | Maximum slope (used also in Steps (5) and (8)) | 1st loop: 3° 2nd loop: 3° or 4.5°s |

Maximum height difference to determine as GP (used also in Step (7)) | 0.5 m | |

Step (6): DTM estimation | Maximum distance of the closest point (same as “Window size” in Step (2)) | 50 m |

Maximum distance of other points | 100 m | |

Window size for mean of DTM | 21 m × 21 m |

**Table 3.**Comparison of filtering errors of TerraScan, Mongus and Žalik’s algorithm (Mongus) [18], and the proposed algorithm for ISPRS benchmark datasets.

Sample | Algorithm | Total (%) | Type I (%) | Type II (%) |
---|---|---|---|---|

11 | TerraScan | 16.14 | 26.66 | 2.00 |

Mongus | 11.01 | 7.32 | 15.98 | |

Proposed | 18.62 | 21.87 | 14.24 | |

12 | TerraScan | 11.55 | 21.49 | 1.12 |

Mongus | 5.17 | 4.23 | 6.15 | |

Proposed | 7.08 | 8.45 | 5.64 | |

21 | TerraScan | 11.56 | 14.30 | 1.95 |

Mongus | 1.98 | 0.01 | 8.87 | |

Proposed | 8.50 | 0.60 | 36.17 | |

22 | TerraScan | 10.78 | 14.51 | 2.56 |

Mongus | 6.56 | 4.97 | 10.09 | |

Proposed | 7.29 | 2.82 | 17.13 | |

23 | TerraScan | 8.01 | 12.92 | 2.54 |

Mongus | 5.83 | 4.38 | 7.45 | |

Proposed | 8.42 | 11.14 | 5.39 | |

24 | TerraScan | 12.97 | 16.38 | 3.98 |

Mongus | 7.98 | 5.69 | 14.04 | |

Proposed | 6.71 | 5.24 | 10.59 | |

31 | TerraScan | 4.85 | 8.36 | 8.97 |

Mongus | 3.34 | 0.21 | 7.00 | |

Proposed | 2.74 | 0.38 | 5.51 | |

41 | TerraScan | 13.15 | 25.10 | 0.74 |

Mongus | 3.71 | 3.39 | 4.03 | |

Proposed | 3.93 | 2.84 | 5.01 | |

42 | TerraScan | 2.55 | 8.00 | 1.39 |

Mongus | 5.72 | 0.06 | 8.06 | |

Proposed | 3.26 | 6.97 | 1.72 |

Process | Parameter | Value Used |
---|---|---|

Classify ground | Max building size | 60 m |

Iteration angle | 3.0° to plane | |

Iteration distance | 0.5 m | |

[Option] Reduce iteration angle | Off | |

[Option] Stop triangulation | Off |

## Share and Cite

**MDPI and ACS Style**

Susaki, J. Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation. *Remote Sens.* **2012**, *4*, 1804-1819.
https://doi.org/10.3390/rs4061804

**AMA Style**

Susaki J. Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation. *Remote Sensing*. 2012; 4(6):1804-1819.
https://doi.org/10.3390/rs4061804

**Chicago/Turabian Style**

Susaki, Junichi. 2012. "Adaptive Slope Filtering of Airborne LiDAR Data in Urban Areas for Digital Terrain Model (DTM) Generation" *Remote Sensing* 4, no. 6: 1804-1819.
https://doi.org/10.3390/rs4061804