Abstract
A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79% for the Paris-rue-Madame point cloud bench mark data set.
Original language | English |
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Pages (from-to) | 411-416 |
Number of pages | 6 |
Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Volume | 42 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2018 |
Event | ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change” - Delft, Netherlands Duration: 1 Oct 2018 → 5 Oct 2018 |
Keywords
- Classification
- Feature extraction
- Knowledge-based system
- Mobile laser scanning
- Point clouds