Issues of Tree Species Classification from LiDAR Data Using Deep Learning Model

2021 ◽  
pp. 319-324
Author(s):  
Elizaveta K. Sakharova ◽  
Dana D. Nurlyeva ◽  
Antonina A. Fedorova ◽  
Alexey R. Yakubov ◽  
Anton I. Kanev
Keyword(s):  
Deep Learning ◽  
Tree Species ◽  
Learning Model ◽  
Lidar Data ◽  
Species Classification ◽  
Tree Species Classification ◽  
Deep Learning Model

scholarly journals Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1284 ◽  
Author(s):  
Sean Hartling ◽  
Vasit Sagan ◽  
Paheding Sidike ◽  
Maitiniyazi Maimaitijiang ◽  
Joshua Carron
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Tree Species ◽  
Training Sample ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Urban Tree ◽  
Tree Species Classification ◽  
Fusion Approach

Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification. The increased availability of high spatial resolution multispectral satellite imagery and LiDAR datasets combined with the recent evolution of deep learning within remote sensing for object detection and scene classification, provide promising opportunities to map individual tree species with greater accuracy and resolution. However, there are knowledge gaps that are related to the contribution of Worldview-3 SWIR bands, very high resolution PAN band and LiDAR data in detailed tree species mapping. Additionally, contemporary deep learning methods are hampered by lack of training samples and difficulties of preparing training data. The objective of this study was to examine the potential of a novel deep learning method, Dense Convolutional Network (DenseNet), to identify dominant individual tree species in a complex urban environment within a fused image of WorldView-2 VNIR, Worldview-3 SWIR and LiDAR datasets. DenseNet results were compared against two popular machine classifiers in remote sensing image analysis, Random Forest (RF) and Support Vector Machine (SVM). Our results demonstrated that: (1) utilizing a data fusion approach beginning with VNIR and adding SWIR, LiDAR, and panchromatic (PAN) bands increased the overall accuracy of the DenseNet classifier from 75.9% to 76.8%, 81.1% and 82.6%, respectively. (2) DenseNet significantly outperformed RF and SVM for the classification of eight dominant tree species with an overall accuracy of 82.6%, compared to 51.8% and 52% for SVM and RF classifiers, respectively. (3) DenseNet maintained superior performance over RF and SVM classifiers under restricted training sample quantities which is a major limiting factor for deep learning techniques. Overall, the study reveals that DenseNet is more effective for urban tree species classification as it outperforms the popular RF and SVM techniques when working with highly complex image scenes regardless of training sample size.

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 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.

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.

scholarly journals FUSION OF LIDAR AND HYPERSPECTRAL DATA FOR SEMANTIC SEGMENTATION OF FOREST TREE SPECIES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 487-494
Author(s):  
E. Tusa ◽  
J. M. Monnet ◽  
J. B. Barré ◽  
M. Dalla Mura ◽  
J. Chanussot
Keyword(s):  
Tree Species ◽  
Semantic Segmentation ◽  
Forest Tree ◽  
Feature Reduction ◽  
Lidar Data ◽  
Topographic Effects ◽  
Species Classification ◽  
Forest Tree Species ◽  
Tree Species Classification ◽  
Feature Descriptors

Abstract. Hyperspectral images (HI) and Light Detection and Ranging (LiDAR) provide high resolution radiometric and geometric information for monitoring forests at individual tree crown (ITC) level. It has many important applications for sustainable forest management, biodiversity assessment and healthy ecosystem preservation. However, the integration of different remote sensing modalities is a challenging task for tree species classification due to different artifacts such as the lighting variability, the topographic effects and the atmospheric conditions of the data acquisition. The characterization of ITC can benefit from the extraction and selection of robust feature descriptors that solve these issues. This paper aims to investigate the integration of feature descriptors from HI and LiDAR by using the intra-set and inter-set feature importance for the semantic segmentation of forest tree species. A fusion methodology is proposed between high-density LiDAR data – (20 pulses m−2) and VNIR HI – (160 bands and 0.80 m spatial resolution) acquired on French temperate forests along an altitude gradient. The proposed scheme has three inputs: the field inventory information, the HI and the LiDAR data. Our approach can be described in nine stages: polygon projection, non-overlapping pixel selection, vegetation and shadow removal, LiDAR feature extraction, height mask, robust PCA (rPCA), feature reduction and classification. The overall accuracy of tree species classification at pixel-level was 68.9% by using random forest (RF) classifier. Our approach showed that 74.0% of trees were correctly assigned overall, by having conifer species such as Norway Spruce (Picea abies) with a producer’s accuracy of 97.4%.

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