TREE SPECIES CLASSIFICATION BASED ON AIRBORNE LIDAR AND HYPERSPECTRAL DATA

被引：2

作者：

Lu, Xukun ^{[1
]}

Liu, Gang ^{[1
]}

Ning, Silan ^{[2
]}

Su, Zhonghua ^{[2
]}

He, Ze ^{[2
]}

机构：

[1] China Acad Elect & Informat Technol, 11 Shuangyuan Rd, Beijing 10041, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Resources & Environm, 2006 Xiyuan Ave, Chengdu 611731, Peoples R China

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

hyperspectral image; airborne LiDAR; feature extraction; tree species classification; INDIVIDUAL TREES; BIOMASS;

D O I：

10.1109/IGARSS39084.2020.9324266

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Forest resources are of great significance in regulating climate, maintaining biodiversity, and providing ecological products. Accurate identification of tree species is the basis for research and utilization of forest resources. This study combined the characteristics of multi-source data, based on the AISA EAGLE II hyperspectral images and airborne LiDAR point clouds which were obtained in August, 2016. Point cloud characteristics, spectral and texture characteristics were extracted from both datasets. Then SVM was used to classify the main tree species of Genhe experimental area. The results showed that tree species classification accuracy can be improved by using airborne LiDAR and hyperspectral image features.

引用

页码：2787 / 2790

页数：4

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