Airborne small-footprint full-waveform LiDAR data for urban land cover classification

Abstract

Airborne small-footprint full-waveform LiDAR data have a unique ability to characterize the landscape because it contains rich horizontal and vertical information. However, a few studies have fully explored its role in distinguishing different objects in the urban area. In this study, we examined the efficacy of small-footprint full-waveform LiDAR data on urban land cover classification. The study area is located in a suburban area in Beijing, China. Eight land cover classes were included: impervious ground, bare soil, grass, crop, tree, low building, high building, and water. We first decomposed waveform LiDAR data, from which a set of features were extracted. These features were related to amplitude, echo width, mixed ratio, height, symmetry, and vertical distribution. Then, we used a random forest classifier to evaluate the importance of these features and conduct the urban land cover classification. Finally, we assessed the classification accuracy based on a confusion matrix. Results showed that Afirst was the most important feature for urban land cover classification, and the other seven features, namely, ωfirst, HEavg, nHEavg, RAω, SYMS, Srise, and ωRf_fl, also played important roles in classification. The random forest classifier yielded an overall classification accuracy of 94.7%, which was higher than those from previous LiDAR-derived classifications. The results indicated that full-waveform LiDAR data could be used for high-precision urban land cover classification, and the proposed features could help improve the classification accuracy.