object-based building boundary extraction from lidar data you shao and samsung lim
TRANSCRIPT
Most filtering algorithms require rasterisation of lidar data•Additional computing overhead•Loss of information•Increase of uncertainty
Our method•No rasterisation•Adaptive window size•Morphological filtering•DTM generation and building detection
Research Objectives
•The UNSW Campus (1 km x 2 km)•Small residential buildings, high-rise buildings, steep roads, tall trees and large green areas•Lidar data (X, Y, Z, I)•Airborne imagery (R, G, B)•2-year gap between the two datasets
Study Area and Datasets
•Employ dilation and erosion to find the maximum or minimum measurements in lidar points•An adaptive window size indicator is developed to detect building rooftops and modify the window size automatically•An approximate size of a building can be detected by measuring the elevation rise and fall, and therefore the window size can be changed accordingly
Proposed Adaptive Filtering
•Normalised Difference Vegetation Index (NDVI) to remove vegetation•Alpha-shape to form building outlines•Grid-based algorithm•Modified convex hull algorithm•Fine-tuning with adjustable parameters to remove small residuals
Approaches to Building Detection
B1 B2 B3 B4 B5 B6 B7 B8 MeanAlpha-shape 0.84 0.56 0.39 0.49 0.99 0.88 0.95 0.82 0.740
Modified convex
hull0.84 0.48 0.42 0.49 1.11 0.78 1.01 0.84 0.746
Grid-based 0.83 0.49 0.46 0.47 1.1 0.9 0.97 0.79 0.751
Horizontal RMSE (m, 1σ)
•The proposed algorithm is suitable for steep urban areas with varying building sizes•The required parameters of the proposed algorithm can be automatically determined•The test results show that the proposed algorithm is able to classify ground points with a vertical accuracy of 36 cm, a horizontal accuracy of 75 cm and a commission error less than 6%•As for multi-rooftop buildings, it is difficult to determine the actual size of the building; however, this problem can be solved by the proposed dual-direction process
Concluding Remarks