Filtering Airborne Laser Scanner Data

Abstract

Filtering the airborne laser scanner data is challenging due to the complex distribution of objects on Earth’s surface and it is still in development stage. This problem has been investigated so far with varieties of algorithms, but they suffer from different magnitudes of drawbacks. This study proposed a new and improved hybrid method based on multi-resolution analysis. Wavelet was adopted in this multi-resolution clustering approach. It enabled the classification of objects based on their size and the efficiency to filter out unwanted information at a specific resolution, and the proposed algorithm is named the ALSwave (Airborne Laser Scanner Wavelet) method. ALSwave has been tested on two data sets acquired over the urban areas of Tokyo, Japan and Stuttgart, Germany. The results showed a well-filtered, bare earth surface coupled with acceptable computational time. The accuracy assessment was carried out by comparison between the filtered bare earth surface by ALSwave and the manually filtered surface. The Root Mean Square Error (RMSE) follows a linear relationship with respect to terrain slope. This wavelet-based approach has opened a new way to filter the raw laser data that subsequently generates fast and more accurate digital terrain models.