Abstract
Abstract. In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.
Highlights
In the last decade, Light Detection And Ranging (LiDAR) has become an important source for acquisition of the 3D information of targets
This paper aims to study the land cover classification using full-waveform LiDAR data
In 2008, Reitberger et al described an unsupervised species classification method based on features that were derived by waveform decomposition of full waveform LiDAR data
Summary
Light Detection And Ranging (LiDAR) has become an important source for acquisition of the 3D information of targets. It has been widely applied in many fields of remote sensing, such as, environment monitoring, disaster assessment, land cover classification. This paper aims to study the land cover classification using full-waveform LiDAR data. Some scholars have studied land cover classification based on full-waveform features. In 2008, Straub et al presented a processing procedure for automated delineation and classification of forest and non-forest vegetation which was solely using full waveform laser scanner data as input. In 2008, Reitberger et al described an unsupervised species classification method based on features that were derived by waveform decomposition of full waveform LiDAR data.
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