Unsupervised extraction of urban features from airborne lidar data by using self-organizing maps. Issue 371 (3rd March 2020)

• Record Type: Journal Article
• Authors: Sen, Alper; Suleymanoglu, Baris; Soycan, Metin
• Is Part Of: Survey review. Volume 52:Issue 371(2020)
• Page Start: 150
• Page End: 158
• Publication Date: 2020-03-03
• Subjects: Lidar -- SOM -- Extraction -- Adaptive TIN -- Filtering -- Weighting; Surveying -- Periodicals; Great Britain -- Surveys -- Periodicals; 526.9
• DOI: 10.1080/00396265.2018.1532704
• Languages: English
• ISSNs: 0039-6265

Abstract

Abstract : The extraction of artificial and natural features using light detection and ranging (Lidar) data is a fundamental task in many fields of research for environmental science. In this study, the possibility of using self-organising maps (SOM), which is an unsupervised artificial neural network classification method to extract the bare earth surface and features from airborne Lidar data, was investigated for two different urban areas. The effect of the enlargement of the study area was analysed using the proposed approach. The appropriate weights of SOM inputs, which are 3D coordinates and intensity, obtained from a Lidar point cloud were determined by using Pearson's chi-squared independence test. The weighted SOM feature extraction performance was better than that of the unweighted SOM. The filtering results of SOM to separate ground and non-ground data were also compared with those obtained by the adaptive TIN filtering algorithm. Most of the non-ground features could be removed by the weighted SOM.