Classification of LiDar Data Using Window-Based Techniques

Abstract

Given LiDAR maps, we focus on identifying anthropologically relevant ditches automatically on the map. Archeologists can identify these features visually at the site, but approaches based on remotely sensed data would be preferable. This paper proposes an algorithm that uses window-based technique to read the characteristics of each region from maps, whose ditches are already identified, regressively, and then builds histograms to represent the different characters of each region. A classification model is then built based on the histograms and used to predict future data. The goal is to produce a large training data set using window-based technology and use it to classify future data. We demonstrated our algorithm successfully identifies target regions efficiently on real LiDAR maps.