Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

Hongchao Ma; Zhan Cai; Liang Zhang

doi:10.1117/1.JRS.12.016021

14 February 2018 Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer

Hongchao Ma, Zhan Cai, Liang Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016021 (February 2018). https://doi.org/10.1117/1.JRS.12.016021

Abstract

This paper discusses airborne light detection and ranging (LiDAR) point cloud filtering (a binary classification problem) from the machine learning point of view. We compared three supervised classifiers for point cloud filtering, namely, Adaptive Boosting, support vector machine, and random forest (RF). Nineteen features were generated from raw LiDAR point cloud based on height and other geometric information within a given neighborhood. The test datasets issued by the International Society for Photogrammetry and Remote Sensing (ISPRS) were used to evaluate the performance of the three filtering algorithms; RF showed the best results with an average total error of 5.50%. The paper also makes tentative exploration in the application of transfer learning theory to point cloud filtering, which has not been introduced into the LiDAR field to the authors’ knowledge. We performed filtering of three datasets from real projects carried out in China with RF models constructed by learning from the 15 ISPRS datasets and then transferred with little to no change of the parameters. Reliable results were achieved, especially in rural area (overall accuracy achieved 95.64%), indicating the feasibility of model transfer in the context of point cloud filtering for both easy automation and acceptable accuracy.

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Hongchao Ma, Zhan Cai, and Liang Zhang "Comparison of the filtering models for airborne LiDAR data by three classifiers with exploration on model transfer," Journal of Applied Remote Sensing 12(1), 016021 (14 February 2018). https://doi.org/10.1117/1.JRS.12.016021

Received: 2 August 2017; Accepted: 17 January 2018; Published: 14 February 2018

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