Extraction and Analysis of Feature Layers from Airborne LiDAR Point Clouds in Downtown Urban Landscapes

Abstract

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the ground surface and non-ground features such as vegetations, trees, roads and buildings etc. Extraction of non-ground features from airborne LiDAR measurements has been a main objective for researches in the past twenty years. This study aimed at exploring the different methods for extraction of non-ground features from airborne LiDAR point clouds for creation of reliable feature layers that can be employed in a wide range of engineering and environmental applications. Also, undertaking comparative study for the application of Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter on airborne LiDAR Digital Surface Models (DSMs) of varying window sizes in extraction of reliable feature layers have been a main objective of this study. Airborne LiDAR dataset captured over the downtown of the City of Vaihingen in Germany has been exploited in the study. Visual analysis of the extracted feature layers has shown that Gaussian low pass filter of 3x3 window size has removed a small range of non-ground features heights while detailed and structured feature layer has been obtained using window size of 21x21. Additionally, the focal analysis mean filter has achieved better removal of non-ground features compared to Gaussian low pass filter at similar window sizes where better representation of the downtown landscape has been obtained using window size of 21x21 and larger. On the other hand, visual analysis has not shown clear differences between the feature layers extracted using DTM slopebased filter due to changing the filter window sizes. Statistical analysis has indicated that the ranges of elevations in the feature layers from the different examined filters have increased with increasing the filter window size till 15x15. Also, the standard deviations of the feature layers have increased due to increasing the window sizes of Gaussian low pass and focal analysis mean filters, however increasing the window size of the DTM slope-based filter has produced slight increases in the standard deviation. Additionally, increasing the window size of Gaussian low pass and the focal analysis mean filters has produced feature layers of skewness approaching to zero with window size of 21x21. This has referred to feature layers of symmetrical Gaussian normal distribution curves. Moreover, dramatic decreases have occurred in the feature layer kurtosis due to increases in the filter window sizes till window sizes of 21x21 in the cases of Gaussian low pass and focal analysis mean filters which referred to more consistent and outlier free feature layers.