@inproceedings{c30b6a3bcc9e4e479daf347330a387e6,
title = "A semantic labeling framework for ALS point clouds based on discretization and CNN",
abstract = "The airborne laser scanning (ALS) point cloud has drawn increasing attention thanks to its capability to quickly acquire large-scale and high-precision ground information. Due to the complexity of observed scenes and the irregularity of point distribution, the semantic labeling of ALS point clouds is extremely challenging. In this paper, we introduce an efficient discretization based framework according to the geometric character of ALS point clouds, and propose an original intraclass weighted cross entropy loss function to solve the problem of data imbalance. We evaluate our framework on the ISPRS (International Society for Photogrammetry and Remote Sensing) 3D Semantic Labeling dataset. The experimental results show that the proposed method has achieved a new state-of-the-art performance in terms of overall accuracy (85.3\%) and average F1 score (74.1\%).",
keywords = "ALS point clouds, CNN, Discretization, Semantic labeling",
author = "Xingtao Wang and Xiaopeng Fan and Debin Zhao",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020 ; Conference date: 01-12-2020 Through 04-12-2020",
year = "2020",
month = dec,
day = "1",
doi = "10.1109/VCIP49819.2020.9301759",
language = "英语",
series = "2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "58--61",
booktitle = "2020 IEEE International Conference on Visual Communications and Image Processing, VCIP 2020",
address = "美国",
}