Name
Abstract | ||
|---|---|---|
Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/rs8090730 | REMOTE SENSING |
Keywords | Field | DocType |
deep learning,convolutional neural network (CNN),digital terrain model (DTM),ALS,ground point classification | Computer vision,Convolutional neural network,Word error rate,Terrain,Remote sensing,Filter (signal processing),Feature extraction,Artificial intelligence,Deep learning,Elevation,Geology,Point cloud | Journal |
Volume | Issue | ISSN |
8 | 9 | 2072-4292 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyun Hu | 1 | 79 | 8.87 |
| Yi Yuan | 2 | 72 | 7.03 |