Abstract
In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.
Original language | English |
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Pages (from-to) | 169-176 |
Number of pages | 8 |
Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Volume | 3 |
DOIs | |
State | Published - 2 Jun 2016 |
Externally published | Yes |
Event | 23rd International Society for Photogrammetry and Remote Sensing Congress, ISPRS 2016 - Prague, Czech Republic Duration: 12 Jul 2016 → 19 Jul 2016 |
Keywords
- ALS
- Classification
- Features
- LiDAR
- Multi-Scale
- Point Cloud