scholarly journals 3D-CNN BASED TREE SPECIES CLASSIFICATION USING MOBILE LIDAR DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 989-993
Author(s):  
H. Guan ◽  
Y. Yu ◽  
W. Yan ◽  
D. Li ◽  
J. Li
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Lidar Data ◽  
Species Classification ◽  
Section Data ◽  
Tree Species Classification ◽  
Intensity Information ◽  
Segmentation Approach ◽  
3D Cnn ◽  
Individual Trees

<p><strong>Abstract.</strong> Our work addresses the problem of classifying tree species from mobile LiDAR data. The work is a two step-wise strategy, including tree segmentation and tree species classification. In the tree segmentation step, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, individual trees are segmented via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. In the tree species classification, a voxel-based 3D convolutional neural network (3D-CNN) model is developed based on intensity information. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree classification method. Qualitative analysis shows that our algorithm achieves a good performance.</p>

scholarly journals FEATURE RELEVANCE ASSESSMENT OF MULTISPECTRAL AIRBORNE LIDAR DATA FOR TREE SPECIES CLASSIFICATION

2018 ◽  
Vol XLII-3 ◽  
pp. 31-34 ◽  
Author(s):  
N. Amiri ◽  
M. Heurich ◽  
P. Krzystek ◽  
A. K. Skidmore
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Species Classification ◽  
Tree Species Classification ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Species Mapping ◽  
Spectral Channels

The presented experiment investigates the potential of Multispectral Laser Scanning (MLS) point clouds for single tree species classification. The basic idea is to simulate a MLS sensor by combining two different Lidar sensors providing three different wavelngthes. The available data were acquired in the summer 2016 at the same date in a leaf-on condition with an average point density of 37&amp;thinsp;points/m<sup>2</sup>. For the purpose of classification, we segmented the combined 3D point clouds consisiting of three different spectral channels into 3D clusters using Normalized Cut segmentation approach. Then, we extracted four group of features from the 3D point cloud space. Once a varity of features has been extracted, we applied forward stepwise feature selection in order to reduce the number of irrelevant or redundant features. For the classification, we used multinomial logestic regression with <i>L<sub>1</sub></i> regularization. Our study is conducted using 586 ground measured single trees from 20 sample plots in the Bavarian Forest National Park, in Germany. Due to lack of reference data for some rare species, we focused on four classes of species. The results show an improvement between 4&amp;ndash;10&amp;thinsp;pp for the tree species classification by using MLS data in comparison to a single wavelength based approach. A cross validated (15-fold) accuracy of 0.75 can be achieved when all feature sets from three different spectral channels are used. Our results cleary indicates that the use of MLS point clouds has great potential to improve detailed forest species mapping.

scholarly journals Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020 ◽  
Author(s):  
Somayeh Nezami ◽  
Ehsan Khoramshahi ◽  
Olli Nevalainen ◽  
Ilkka Pölönen ◽  
Eija Honkavaara
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tree Species ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Test Site ◽  
Species Classification ◽  
Tree Species Classification ◽  
3D Cnn ◽  
Individual Trees

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, bio-mass estimation, etc. Deep Neural Networks (DNN) have shown superior results when comparing with conventional machine learning methods such as Multi-Layer Perceptron (MLP) in cases of huge input data. The objective of this research was to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, RGB channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

scholarly journals Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

2020 ◽  
Vol 12 (7) ◽  
pp. 1070 ◽  
Author(s):  
Somayeh Nezami ◽  
Ehsan Khoramshahi ◽  
Olli Nevalainen ◽  
Ilkka Pölönen ◽  
Eija Honkavaara
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tree Species ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Test Site ◽  
Biomass Estimation ◽  
Species Classification ◽  
Tree Species Classification ◽  
3D Cnn

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

Single tree species classification from Terrestrial Laser Scanning data for forest inventory

2013 ◽  
Vol 34 (16) ◽  
pp. 2144-2150 ◽  
Author(s):  
Ahlem Othmani ◽  
Lew F.C. Lew Yan Voon ◽  
Christophe Stolz ◽  
Alexandre Piboule
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Species Classification ◽  
Single Tree ◽  
Tree Species Classification

scholarly journals Tree species classification in Norway from airborne hyperspectral and airborne laser scanning data

2018 ◽  
Vol 51 (1) ◽  
pp. 336-351 ◽  
Author(s):  
Øivind Due Trier ◽  
Arnt-Børre Salberg ◽  
Martin Kermit ◽  
Øystein Rudjord ◽  
Terje Gobakken ◽  
...  
Keyword(s):  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Airborne Laser
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