An algorithm for building extraction from airborne LiDAR data under adaptive local spatial-spectral consistency

doi:10.11947/j.AGCS.2024.20230517

Abstract

Abstract:

All the existing studies use the global statistical characteristics of laser reflection intensity of buildings to aid the extraction of buildings from airborne LiDAR data, but this solution cannot meet the needs of comprehensive and accurate extraction of buildings with different spectra in large-scale complex urban scenes. Therefore, a building extraction method from airborne LiDAR data based on adaptive local spatial-spectral consistency is developed. The proposed method first converts raw airborne LiDAR data into 3D image. Then, the seeds are selected according to the characteristics of building elevation jump and edge approaching straight line. Subsequently, the connected components that are spatially and spectrally consistent with the seedsare labeled as the building roof, in which the spectral consistency is given by the statistical intensity properties of an individual building. Finally, the building facade is extracted by combining the spatial constraint of the extracted building roof and the local intensity consistency constraints. This method solves the problem of accurate extraction of buildings that do not conform to the global statistical characteristic of the spectrum by self-adapting to the local spectrum of each individual building, improves the use value of point cloud spectral information, and thus broadens the application scenarios of point cloud spectral information. Three airborne LiDAR datasets of urban scene with different complexities provided by International Association for Photogrammetry and Remote Sensing are used to test the feasibility and effectiveness of the proposed method. The experimental results show that the proposed method can excellently extract buildings in scenes with different complexities. The average completeness, accuracy and quality of the building extraction results are 99.0%, 98.0% and 96.8%, respectively, which are obviously better than the traditional building extraction method using the global statistical properties of the spectrum.

Key words: airborne LiDAR, building extraction, spatial-spectral consistency, point cloud, global statistical characteristic

CLC Number:

P236

Liying WANG, Kangli ZHANG, Xinao LI, Ze YOU, Yong FENG. An algorithm for building extraction from airborne LiDAR data under adaptive local spatial-spectral consistency[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(12): 2349-2360.

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区域	空间邻域	一类误差/（%）	二类误差/（%）	总误差/（%）
Area1	6	84.87	0.91	43.12
	18	11.56	32.78	22.11
	26	6.52	5.75	6.14
	56	2.26	4.93	3.59
	80	4.85	9.65	7.23
	124	4.38	14.59	9.46
Area2	6	50.93	2.97	15.88
	18	18.25	2.21	6.53
	26	14.44	3.39	6.37
	56	3.90	0.65	1.53
	80	4.48	1.10	2.01
	124	3.82	1.05	1.80
Area3	6	77.50	1.45	32.98
	18	24.89	5.64	13.62
	26	16.09	3.12	8.50
	56	4.31	2.83	3.45
	80	4.92	8.34	6.92
	124	4.97	18.64	12.98

区域	完整率	正确率	质量
Area1	99.0	98.0	97.0
Area2	99.2	98.1	96.4
Area3	98.9	97.9	96.9

建筑物提取算法	Area1			Area2			Area3
建筑物提取算法	完整率	正确率	质量	完整率	正确率	质量	完整率	正确率	质量
文献[28]	89.4	94.5	84.9	91.0	95.0	86.8	87.7	94.5	83.4
文献[29]	91.4	94.3	86.6	86.5	93.6	81.7	88.3	99.0	87.5
文献[30]	90.4	94.2	85.7	93.5	94.8	88.9	91.0	93.0	85.2
文献[31]	85.4	86.4	75.4	88.8	84.5	76.4	89.9	84.7	77.4
文献[19]	90.6	94.5	86.0	93.8	98.7	92.7	85.5	94.9	85.7
本文方法	99.0	98.0	97.0	99.2	98.1	96.4	98.9	97.9	96.9