scholarly journals Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

2021 ◽  
Vol 13 (23) ◽  
pp. 4750
Author(s):  
Jianchang Chen ◽  
Yiming Chen ◽  
Zhengjun Liu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Laser Scanning ◽  
Species Classification ◽  
Tree Species Classification ◽  
Network Mapping

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

scholarly journals Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 818
Author(s):  
Yanbiao Xi ◽  
Chunying Ren ◽  
Zongming Wang ◽  
Shiqing Wei ◽  
Jialing Bai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Learning Algorithms ◽  
Spectral Characteristics ◽  
Great Influence ◽  
Hyperspectral Images ◽  
Changbai Mountains ◽  
Species Classification ◽  
Tree Species Classification

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

Tree Species Classification of LiDAR Data based on 3D Deep Learning

Measurement ◽  
2021 ◽  
pp. 109301
Author(s):  
Maohua Liu ◽  
Ziwei Han ◽  
Yiming Chen ◽  
Zhengjun Liu ◽  
Yanshun Han
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Lidar Data ◽  
Species Classification ◽  
Tree Species Classification

scholarly journals An Improved Res-UNet Model for Tree Species Classification Using Airborne High-Resolution Images

2020 ◽  
Vol 12 (7) ◽  
pp. 1128 ◽  
Author(s):  
Kaili Cao ◽  
Xiaoli Zhang
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Classification Accuracy ◽  
Southern China ◽  
Forest Tree ◽  
Image Features ◽  
Support Vector ◽  
Learning Technology ◽  
Species Classification ◽  
Tree Species Classification

Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and generally low accuracy, whereas deep learning technology can automatically extract image features to achieve end-to-end classification. Therefore, a tree classification method based on deep learning is proposed in this study. This method combines the semantic segmentation network U-Net and the feature extraction network ResNet into an improved Res-UNet network, where the convolutional layer of the U-Net network is represented by the residual unit of ResNet, and linear interpolation is used instead of deconvolution in each upsampling layer. At the output of the network, conditional random fields are used for post-processing. This network model is used to perform classification experiments on airborne orthophotos of Nanning Gaofeng Forest Farm in Guangxi, China. The results are then compared with those of U-Net and ResNet networks. The proposed method exhibits higher classification accuracy with an overall classification accuracy of 87%. Thus, the proposed model can effectively implement forest tree species classification and provide new opportunities for tree species classification in southern China.

scholarly journals EVALUATING A CONVOLUTIONAL NEURAL NETWORK FOR FEATURE EXTRACTION AND TREE SPECIES CLASSIFICATION USING UAV-HYPERSPECTRAL IMAGES

2020 ◽  
Vol V-3-2020 ◽  
pp. 193-199
Author(s):  
C. Sothe ◽  
L. E. C. la Rosa ◽  
C. M. de Almeida ◽  
A. Gonsamo ◽  
M. B. Schimalski ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Tree Species ◽  
Data Augmentation ◽  
Support Vector ◽  
Species Classification ◽  
Tree Species Classification ◽  
Spatial Features

Abstract. The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced feature extraction and classification methods. Different from the traditional feature extraction methods, that highly depend on user’s knowledge, the convolutional neural network (CNN)-based method can automatically learn and extract the spatial-related features layer by layer. However, in order to capture significant features of the data, the CNN classifier requires a large number of training samples, which are hardly available when dealing with tree species in tropical forests. This study investigated the following topics concerning the classification of 14 tree species in a subtropical forest area of Southern Brazil: i) the performance of the CNN method associated with a previous step to increase and balance the sample set (data augmentation) for tree species classification as compared to the conventional machine learning methods support vector machine (SVM) and random forest (RF) using the original training data; ii) the performance of the SVM and RF classifiers when associated with a data augmentation step and spatial features extracted from a CNN. Results showed that the CNN classifier outperformed the conventional SVM and RF classifiers, reaching an overall accuracy (OA) of 84.37% and Kappa of 0.82. The SVM and RF had a poor accuracy with the original spectral bands (OA 62.67% and 59.24%) but presented an increase between 14% and 21% in OA when associated with a data augmentation and spatial features extracted from a CNN.

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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.

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