Airborne small-footprint full-waveform LiDAR data for urban land cover classification - Qin, Haiming; Zhou, Weiqi; Zhao, Wenhui (Frontiers in Environmental Science, 2022)
Airborne small-footprint full-waveform LiDAR data has a unique ability to characterize the landscape because it contains rich horizontal and vertical information. However, few studies have fully explored its role in distinguishing different objects in 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 at 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 first, HEavg, nHEavg, RA, SYMS, Srise, and Rf_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 to improve the classification accuracy.