TREE SPECIES CLASSIFICATION BASED ON AIRBORNE LIDAR AND HYPERSPECTRAL DATA

被引：1

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

共 50 条

[21] Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks
Mayra, Janne
Keski-Saari, Sarita
Kivinen, Sonja
Tanhuanpaa, Topi
Hurskainen, Pekka
Kullberg, Peter
Poikolainen, Laura
Viinikka, Arto
Tuominen, Sakari
Kumpula, Timo
Vihervaara, Petteri
REMOTE SENSING OF ENVIRONMENT, 2021, 256
[22] TREE SPECIES CLASSIFICATION USING AIRBORNE HYPERSPECTRAL DATA IN SUBTROPICAL MOUNTAINOUS FOREST
Jia, Wen
Pang, Yong
Meng, Shili
Ju, Hongbo
Li, Zengyuan
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 2284 - 2287
[23] Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data
Maschler, Julia
Atzberger, Clement
Immitzer, Markus
REMOTE SENSING, 2018, 10 (08)
[24] Mapping Individual Tree Species in an Urban Forest Using Airborne Lidar Data and Hyperspectral Imagery
Zhang, Caiyun
Qiu, Fang
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2012, 78 (10): : 1079 - 1087
[25] Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data
Liu, Luxia
Coops, Nicholas C.
Aven, Neal W.
Pang, Yong
REMOTE SENSING OF ENVIRONMENT, 2017, 200 : 170 - 182
[26] Semi-automated tree species classification based on roughness parameters using airborne lidar data
Novo, Ana
Gonzalez-Jorge, Higinio
Comesana-Cebral, Lino-Jose
Lorenzo, Henrique
Martinez-Sanchez, Joaquin
DYNA, 2022, 97 (05): : 528 - 534
[27] Subtropical Forest Tree Species Classification Based on 3D-CNN for Airborne Hyperspectral Data
Zhao L.
Zhang X.
Wu Y.
Zhang B.
Linye Kexue/Scientia Silvae Sinicae, 2020, 56 (11): : 97 - 107
[28] A HYBRID APPROACH FOR TREE CLASSIFICATION IN AIRBORNE LIDAR DATA
Li, Xiaoling
Zeng, Wenjun
Duan, Ye
2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 2183 - 2187
[29] Tree Species Classification in a Complex Brazilian Tropical Forest Using Hyperspectral and LiDAR Data
Martins-Neto, Rorai Pereira
Tommaselli, Antonio Maria Garcia
Imai, Nilton Nobuhiro
Honkavaara, Eija
Miltiadou, Milto
Saito Moriya, Erika Akemi
David, Hassan Camil
FORESTS, 2023, 14 (05):
[30] Tree species classification using plant functional traits from LiDAR and hyperspectral data
Shi, Yifang
Skidmore, Andrew K.
Wang, Tiejun
Holzwarth, Stefanie
Heiden, Uta
Pinnel, Nicole
Zhu, Xi
Heurich, Marco
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2018, 73 : 207 - 219

← 1 2 3 4 5 →