We present an efficient procedure to classify airborne laser scanner data into ground and non-ground. The classification is performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on AHN-3 data resulting in 4.02% of total error, 2.15% of type I error and 6.14% of type II error. We show that this method can be extended to further classify the point cloud into buildings and vegetation.
|Number of pages||1|
|Publication status||Published - 28 Nov 2018|
|Event||NCG symposium 2018 - Wageningen university, Wageningen, Netherlands|
Duration: 29 Nov 2018 → 29 Nov 2018
|Conference||NCG symposium 2018|
|Period||29/11/18 → 29/11/18|
Rizaldy, A., Persello, C., Gevaert, C. M., Oude Elberink, S. J., & Vosselman, G. (2018). Fully Convolutional Networks for Ground Classification from Airborne Laser Scanner data. 5. Abstract from NCG symposium 2018 , Wageningen, Netherlands.