Urban individual tree crown detection research using multispectral image dimensionality reduction with deep learning

Xi, Xiangshu; Xia, Kai; Yang, Yinhui; Du, Xiaochen; Feng, Hailin

doi:10.11834/jrs.20220163

Cited by 5 publications

(3 citation statements)

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“…Regarding the selection of feature extraction bands for deep learning in single tree detection, our study ultimately identified a combination of the red, green, and blue bands as the optimal choice. However, Xi [25] conducted a comparison on the YOLO v3 model, evaluating various band combinations, such as green and blue; near-infrared, red, and green; and blue, red, and near-infrared. The study found that these combinations exhibited the best detection accuracy for urban single tree crowns, with the near-infrared, red, and green bands showing the most effective detection results.…”

Section: Discussionmentioning

confidence: 99%

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Classification of Tree Species in Transmission Line Corridors Based on YOLO v7

Xu,

Wang,

Shi

et al. 2023

Forests

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The effective control of trees in transmission line corridors is crucial to mitigate the damage that they can cause to transmission lines. Investigating trees in these corridors presents a significant challenge, particularly in classifying individual tree species. Although the current deep learning model can segment single tree species, it exhibits low recognition accuracy in areas with dense forest canopies. The detection speed is also subject to limitations. To address these challenges, this study relies on aerial multispectral images obtained from drones as the primary data source. The process begins by extracting single tree crowns and establishing a sample dataset, divided in a 9:1 ratio into training and verification sets. Subsequently, the training set undergoes iterative parameter training using the YOLO v7 network. Once optimal parameters are obtained, the system outputs information on individual tree types. The verification sample set is then employed to assess the accuracy. Simultaneously, the YOLO v4 network model is applied to the same data, and the training results of the YOLO v7 network are compared and analyzed, revealing peak accuracy of 85.42% in recognizing single tree species. This approach provides an effective solution, offering reliable data for the in-depth investigation of trees in transmission line corridors and the accurate monitoring of concealed tree hazards.

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Section: Discussionmentioning

confidence: 99%

“…One involves characteristic band selection, where bands meeting specific conditions are chosen from multiple bands based on certain principles. The other utilizes a feature extraction algorithm to compress multi-band images into 3-band images [25].…”

Section: Construction Of Original Datasetmentioning

confidence: 99%

Classification of Tree Species in Transmission Line Corridors Based on YOLO v7

Xu,

Wang,

Shi

et al. 2023

Forests

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“…The quintessential two-stage network model is the Faster Region-based Convolutional Neural Network (Faster R-CNN) [34]. For tree crown detection, Mubin et al [35] used Faster R-CNN for oil palm tree detection and achieved high accuracy, while Xi et al [36] used multispectral UAV images combined with an improved Faster R-CNN network for individual Ginkgo biloba tree detection on a campus, resulting in good accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Network for Individual Tree Segmentation in UAV Images with a Coupled CSPNet and Attention Mechanism

Lv,

Li,

Mao

et al. 2023

Remote Sensing

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Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.

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UAV-Based Intelligent Detection of Individual Trees in Moso Bamboo Forests With Complex Canopy Structure

Lv,

Zhao,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Urban individual tree crown detection research using multispectral image dimensionality reduction with deep learning

Cited by 5 publications

References 0 publications

Classification of Tree Species in Transmission Line Corridors Based on YOLO v7

Classification of Tree Species in Transmission Line Corridors Based on YOLO v7

A Deep Learning Network for Individual Tree Segmentation in UAV Images with a Coupled CSPNet and Attention Mechanism

UAV-Based Intelligent Detection of Individual Trees in Moso Bamboo Forests With Complex Canopy Structure

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