Tree Species Classification Based on DenseNet Model in UAV High Resolution Images

2021 ◽  
Vol 09 (04) ◽  
pp. 139-145
Author(s):  
泽宇 张
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Species Classification ◽  
Tree Species Classification ◽  
High Resolution Images

scholarly journals RESNET-BASED TREE SPECIES CLASSIFICATION USING UAV IMAGES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 475-481 ◽  
Author(s):  
S. Natesan ◽  
C. Armenakis ◽  
U. Vepakomma
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Tree Species ◽  
Classification Accuracy ◽  
Forest Tree ◽  
Tree Level ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Rgb Images

<p><strong>Abstract.</strong> Tree species classification at individual tree level is a challenging problem in forest management. Deep learning, a cutting-edge technology evolved from Artificial Intelligence, was seen to outperform other techniques when it comes to complex problems such as image classification. In this work, we present a novel method to classify forest tree species through high resolution RGB images acquired with a simple consumer grade camera mounted on a UAV platform using Residual Neural Networks. We used UAV RGB images acquired over three years that varied in numerous acquisition parameters such as season, time, illumination and angle to train the neural network. To begin with, we have experimented with limited data towards the identification of two pine species namely red pine and white pine from the rest of the species. We performed two experiments, first with the images from all three acquisition years and the second with images from only one acquisition year. In the first experiment, we obtained 80% classification accuracy when the trained network was tested on a distinct set of images and in the second experiment, we obtained 51% classification accuracy. As a part of this work, a novel dataset of high-resolution labelled tree species is generated that can be used to conduct further studies involving deep neural networks in forestry.</p>

scholarly journals FOREST STAND SEGMENTATION USING AIRBORNE LIDAR DATA AND VERY HIGH RESOLUTION MULTISPECTRAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 207-214 ◽  
Author(s):  
Clément Dechesne ◽  
Clément Mallet ◽  
Arnaud Le Bris ◽  
Valérie Gouet ◽  
Alexandre Hervieu
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Forest Stand ◽  
Lidar Data ◽  
Multispectral Imagery ◽  
Forest Land ◽  
Species Classification ◽  
Tree Species Classification ◽  
Forest Land Cover ◽  
Very High

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., &ge; 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with &alpha;-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

scholarly journals CNN-Based Tree Species Classification Using High Resolution RGB Image Data from Automated UAV Observations

2020 ◽  
Vol 12 (23) ◽  
pp. 3892
Author(s):  
Sebastian Egli ◽  
Martin Höpke
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Structural Information ◽  
Image Data ◽  
Time Of Day ◽  
Classification Model ◽  
Atmospheric Conditions ◽  
Species Classification ◽  
Tree Species Classification ◽  
Rgb Image

Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%.

scholarly journals FOREST STAND SEGMENTATION USING AIRBORNE LIDAR DATA AND VERY HIGH RESOLUTION MULTISPECTRAL IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 207-214 ◽  
Author(s):  
Clément Dechesne ◽  
Clément Mallet ◽  
Arnaud Le Bris ◽  
Valérie Gouet ◽  
Alexandre Hervieu
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Forest Stand ◽  
Lidar Data ◽  
Multispectral Imagery ◽  
Forest Land ◽  
Species Classification ◽  
Tree Species Classification ◽  
Forest Land Cover ◽  
Very High

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., &ge; 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with &alpha;-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

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.

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