Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV

Natesan, Sowmya; Armenakis, Costas; Vepakomma, Udayalakshmi

doi:10.1139/juvs-2020-0014

Cited by 38 publications

(6 citation statements)

References 67 publications

(94 reference statements)

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“…The robustness of the model will be evaluated in areas of more complex natural forests (mixed trees with different species and ages). Furthermore, we will expand the number of samples and balance the proportion of different tree species in dataset [52,53]. Overall, our proposed ACE R-CNN architecture can provide a reference for higher accuracy individual tree species identification in high-density and complex forest environments.…”

Section: Discussionmentioning

confidence: 99%

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Chai

Lei

et al. 2022

Remote Sensing

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Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.

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Section: Discussionmentioning

confidence: 99%

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Chai

Lei

et al. 2022

Remote Sensing

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“…Four models, including CNN models (ResNet-50 and ConvNeXt-T) and Transformer models (ViT-B and Swin-T), were trained and validated using UAV RGB tree crown images, achieving classification accuracies surpassing 95%. CNN models have been extensively used in forest resource surveys for tree species classification tasks, demonstrating exceptional classification accuracy [22,35]. Transformer models have also started finding applications in plant classification using UAV imagery [36] and exhibit significant potential for future advancements in forest surveys.…”

Section: Discussionmentioning

confidence: 99%

“…Hu, M. et al [21] used a transfer-learning-based approach that fused multiple deep learning models to solve tree species classification in complex backgrounds, attaining an overall average accuracy of 93.75%. Natesan, S. et al [22] used DenseNet for classifying forest tree species at the individual tree level using high-resolution RGB images from UAVs. The validation results demonstrate an accuracy of over 84% in distinguishing coniferous tree species in eastern Canada.…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning

et al. 2023

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We studied the use of self-attention mechanism networks (SAN) and convolutional neural networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) remote sensing imagery in Dongtai Forest Farm, Jiangsu Province, China. We trained and validated representative CNN models, such as ResNet and ConvNeXt, as well as the SAN model, which incorporates Transformer models such as Swin Transformer and Vision Transformer (ViT). Our goal was to compare and evaluate the performance and accuracy of these networks when used in parallel. Due to various factors, such as noise, motion blur, and atmospheric scattering, the quality of low-altitude aerial images may be compromised, resulting in indistinct tree crown edges and deficient texture. To address these issues, we adopted Real-ESRGAN technology for image super-resolution reconstruction. Our results showed that the image dataset after reconstruction improved classification accuracy for both the CNN and Transformer models. The final classification accuracies, validated by ResNet, ConvNeXt, ViT, and Swin Transformer, were 96.71%, 98.70%, 97.88%, and 98.59%, respectively, with corresponding improvements of 1.39%, 1.53%, 0.47%, and 1.18%. Our study highlights the potential benefits of Transformer and CNN for forest tree species classification and the importance of addressing the image quality degradation issues in low-altitude aerial images.

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“…To make accurate predictions under new conditions, the abundance of heterogeneous samples in the data set plays a key role 46 . Ideally, to increase the robustness of the predictions, CNNs used in vegetation monitoring are trained on data sets recorded at different times and, hence, cover a range of phenotypical and phenological variability 15 , 47 .…”

Section: Discussionmentioning

confidence: 99%

Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

Popp,

Kalwij

2023

Sci Rep

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Conventional forest inventories are labour-intensive. This limits the spatial extent and temporal frequency at which woody vegetation is usually monitored. Remote sensing provides cost-effective solutions that enable extensive spatial coverage and high sampling frequency. Recent studies indicate that convolutional neural networks (CNNs) can classify woody forests, plantations, and urban vegetation at the species level using consumer-grade unmanned aerial vehicle (UAV) imagery. However, whether such an approach is feasible in species-rich savanna ecosystems remains unclear. Here, we tested whether small data sets of high-resolution RGB orthomosaics suffice to train U-Net, FC-DenseNet, and DeepLabv3 + in semantic segmentation of savanna tree species. We trained these models on an 18-ha training area and explored whether models could be transferred across space and time. These models could recognise trees in adjacent (mean F1-Score = 0.68) and distant areas (mean F1-Score = 0.61) alike. Over time, a change in plant morphology resulted in a decrease of model accuracy. Our results show that CNN-based tree mapping using consumer-grade UAV imagery is possible in savanna ecosystems. Still, larger and more heterogeneous data sets can further improve model robustness to capture variation in plant morphology across time and space.

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Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV

Cited by 38 publications

References 67 publications

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Tree Species Classification in UAV Remote Sensing Images Based on Super-Resolution Reconstruction and Deep Learning

Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers

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