Geometric integration of high-resolution satellite imagery and airborne LiDAR data for improved geopositioning accuracy in metropolitan areas

2015 ◽  
Vol 109 ◽  
pp. 139-151 ◽  
Author(s):  
Bo Wu ◽  
Shengjun Tang ◽  
Qing Zhu ◽  
Kwan-yuen Tong ◽  
Han Hu ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Metropolitan Areas ◽  
Airborne Lidar ◽  
Geometric Integration ◽  
Lidar Data ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data

scholarly journals CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1697
Author(s):  
Hui Li ◽  
Baoxin Hu ◽  
Qian Li ◽  
Linhai Jing
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Individual Tree ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used.

scholarly journals Topographical changes revealed by high-resolution airborne LiDAR data: The 1999 Tsaoling landslide induced by the Chi–Chi earthquake

2006 ◽  
Vol 88 (3-4) ◽  
pp. 160-172 ◽  
Author(s):  
Rou-Fei Chen ◽  
Kuo-Jen Chang ◽  
Jacques Angelier ◽  
Yu-Chang Chan ◽  
Benoît Deffontaines ◽  
...  
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

High-resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data

2007 ◽  
Vol 32 (10) ◽  
pp. 1574-1592 ◽  
Author(s):  
Anna F. Jones ◽  
Paul A. Brewer ◽  
Eric Johnstone ◽  
Mark G. Macklin
Keyword(s):  
High Resolution ◽  
River Valley ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Geomorphological Mapping ◽  
Airborne Lidar Data
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