Hyperspectral tree species classification with an aid of lidar data

摘要

Classification of tree species is one of the most important applications in remote sensing. A methodology to classify tree species using hyperspectral and LiDAR data is proposed. The data processing consists of shadow correction, individual tree crown delineation, classification by support vector machine (SVM) and postprocessing by a smoothing filter. The authors applied this procedure to the data taken over Tama Forest Science Garden in Tokyo, Japan and classified it into 16 classes of tree species. As a result, the authors achieved classification accuracy of 79 % with 10 % training data, which is 17 % higher than what is obtained by using hyperspectral data only. Shadow correction and morphological processing derived from LiDAR data increase the accuracy by 3 % and 14 %, respectively.