Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery

2017 ◽  
Vol 126 ◽  
pp. 129-145 ◽  
Author(s):  
Clément Dechesne ◽  
Clément Mallet ◽  
Arnaud Le Bris ◽  
Valérie Gouet-Brunet
Keyword(s):  
High Resolution ◽  
Semantic Segmentation ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multispectral Imagery ◽  
Forest Stands ◽  
Pure Species ◽  
Airborne Lidar Data ◽  
Very High

scholarly journals SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM

2017 ◽  
Vol IV-1/W1 ◽  
pp. 141-148 ◽  
Author(s):  
C. Dechesne ◽  
C. Mallet ◽  
A. Le Bris ◽  
V. Gouet-Brunet
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Airborne Lidar ◽  
Forest Stands ◽  
Pure Species ◽  
Individual Tree ◽  
Very High Spatial Resolution ◽  
Very High

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

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

Evaluating the potential of high‐resolution airborne LiDAR data in glaciology

2006 ◽  
Vol 27 (6) ◽  
pp. 1233-1251 ◽  
Author(s):  
N. S. Arnold ◽  
W. G. Rees ◽  
B. J. Devereux ◽  
G. S. Amable
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data
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