COMBINING LIDAR-DERIVED METRICS WITH RGB-NIR IMAGES TO IMPROVE TREE SPECIES CLASSIFICATION IN A TROPICAL URBAN AREA

被引：0

作者：

Ferreira, Matheus P. ^{[1
]}

dos Santos, Daniel R. ^{[1
]}

Ferrari, Felipe ^{[2
]}

Martins, Gabriela B. ^{[1
]}

Feitosa, Raul Q. ^{[2
]}

机构：

[1] Mil Inst Engn IME, Rio De Janeiro, Brazil

[2] Pontif Catholic Univ Rio Janeiro PUC Rio, Rio De Janeiro, Brazil

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Deep learning; semantic segmentation; tree species discrimination; RGB images;

D O I：

10.1109/IGARSS52108.2023.10282421

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Accurate information on urban tree species distribution can reveal insights into how street trees provide ecosystem services like mitigating air pollution and cooling surfaces. Here, we used LiDAR-derived structural properties of individual tree crowns (ITCs) and digital aerial images to classify urban tree species. We fused LiDAR features with RGB-NIR digital aerial images using a fully convolutional neural network. The fusion strategy consisted in stacking one LiDAR feature at a time with RGB-NIR bands. The results show that surface normals of tree leaves improve the F1-score of all species, with the highest increase reaching 13.7 percentage points.

引用

页码：5914 / 5917

页数：4