浙江农林大学学报2018，Vol.35Issue(2)：314-323,10.DOI:10.11833/j.issn.2095-0756.2018.02.016
基于机载激光雷达和高光谱数据的树种识别方法
Automatic identification of tree species based on airborne LiDAR and hyperspectral data
摘要
Abstract
Selection of training samples, a direct factor affecting the accuracy of supervised classification, with a higher spatial resolution image, requires more accurate training samples, but the human-computer interaction capabilities in the selection of training samples is limited. Therefore, in this study, an algorithm was provided for automatic extraction of training samples. Airborne hyperspectral data and LiDAR data were used in Gutian Mountain National Nature Reserve. The hyperspectral data were used to extract training samples automatically and variables of tree species were calculated. According to differences in structure and height of individual trees provided by the canopy height model of LiDAR, a tree height mask was made to help circumvent the problem of different objects with the same spectra and identical objects with different spectra, as far as possi-ble.Then,the spectral angle between each pixel and training sample pixel was calculated and highly pure pix-els at different heights were selected automatically. In addition, a vegetation index and principal component analysis were calculated. The precise classification of tree species was carried out by a support vector machine classifier in the study area. The experiment used a method of stratified-auto sample selection to extract the training samples of broadleaf,Masson pine,Moso bamboo,Chinese fir,and tea-oil tree forests,and then classi-fied these five tree species. Results showed that the combination of hyperspectral data, LiDAR data, and the structure of the insensitive pigment index revealed an overall accuracy of 89.12% and a Kappa coefficient of 0.86. Using a combination of the best variables, the user accuracy was as follows: broadleaf forest--75.00%, Masson pine--100.00%, Moso bamboo--86.36%, Chinese fir--90.91%, and tea-oil tree--96.55%. Therefore, integration of different remote sensing data, stratified-auto sample selection, and hyperspectral variable selec-tion using LiDAR and the structure insensitive pigment index were effective ways for improving tree species classification.关键词
森林测计学/高光谱/激光雷达/分层训练样本自动提取/树种识别/光谱角填图/支持向量机Key words
forest measuration/hyperspectral/LiDAR/stratified-auto samples selection/tree species identifica-tion/spectral angle mapping/support vector machine分类
农业科技引用本文复制引用
陶江玥,刘丽娟,庞勇,李登秋,冯云云,王雪,丁友丽,彭琼,肖文惠..基于机载激光雷达和高光谱数据的树种识别方法[J].浙江农林大学学报,2018,35(2):314-323,10.基金项目
国家级大学生创新创业训练计划项目(201610341013) （201610341013）
国家自然科学基金资助项目(41201365) （41201365）
浙江农林大学科研发展基金资助项目(2014FR004) （2014FR004）
