Abstract
Abstract. In this study, a Random Forest (RF) based land covers classification method is presented to predict the types of land covers in Miyun area. The returned full-waveforms which were acquired by a LiteMapper 5600 airborne LiDAR system were processed, including waveform filtering, waveform decomposition and features extraction. The commonly used features that were distance, intensity, Full Width at Half Maximum (FWHM), skewness and kurtosis were extracted. These waveform features were used as attributes of training data for generating the RF prediction model. The RF prediction model was applied to predict the types of land covers in Miyun area as trees, buildings, farmland and ground. The classification results of these four types of land covers were obtained according to the ground truth information acquired from CCD image data of the same region. The RF classification results were compared with that of SVM method and show better results. The RF classification accuracy reached 89.73% and the classification Kappa was 0.8631.
Highlights
Airborne Light Detection and Ranging (LiDAR) is a welldeveloped technique for 3D terrain modelling, which is finding increased usage in many different areas of application, such as environment monitoring, disaster assessment, and land covers classification
The waveform features reflecting the properties of targets can be retrieved from the waveforms and are extensively used for a large variety of land covers classification (Mallet, 2009)
This paper aims to study the land cover classification using full-waveform LiDAR data
Summary
Airborne Light Detection and Ranging (LiDAR) is a welldeveloped technique for 3D terrain modelling, which is finding increased usage in many different areas of application, such as environment monitoring, disaster assessment, and land covers classification. This paper aims to study the land cover classification using full-waveform LiDAR data. The most commonly used are decision tree, Support vector machines (SVM) and Random Forests (RF) classification methods. Many researchers have investigated the waveform features for land covers classification using SVM (Bretar, 2009; Tseng, 2015). Blomley has used Random Forest classifier for classifying airborne laser scanning data They have demonstrated that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type (Blomley, 2016). RF classification using full-waveform features, i.e. distance, intensity, FWHM (Full Width at Half Maximum), skewness and kurtosis was presented to predict the types of land covers as trees, buildings, farmland and ground.
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