2022
DOI: 10.3390/rs14133035
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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

Abstract: 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… Show more

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Cited by 33 publications
(20 citation statements)
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“…However, the fused RGB and LiDAR features have increased the overall accuracy by 18.49% which is consistent with the results of many studies [17,59]. For example, Li et al [38] have used an improved algorithm to conduct individual tree species identification based on UAV RGB imagery and LiDAR data. And the results have proved that the combination of RGB and LiDAR data is much more suitable for tree species classification.…”
Section: Discussionsupporting
confidence: 78%
See 2 more Smart Citations
“…However, the fused RGB and LiDAR features have increased the overall accuracy by 18.49% which is consistent with the results of many studies [17,59]. For example, Li et al [38] have used an improved algorithm to conduct individual tree species identification based on UAV RGB imagery and LiDAR data. And the results have proved that the combination of RGB and LiDAR data is much more suitable for tree species classification.…”
Section: Discussionsupporting
confidence: 78%
“…studies that integrated UAV-based LiDAR and RGB imagery for tree species classification [36][37][38].…”
Section: Study Areamentioning
confidence: 99%
See 1 more Smart Citation
“…Some methods have improved edge extraction by enhancing the loss function (Zhang et al, 2022), while others have considered incorporating multimodal feature extraction techniques within the Mask-RCNN framework. They simultaneously use texture, color, and elevation information from both RGB and CHM images to improve tree segmentation accuracy (Li et al, 2022). Although deep learning-based instance segmentation methods for singletree segmentation show significant improvements over traditional image segmentation methods in terms of robustness and generalization, most supervised deep learning methods suffer from a fundamental limitation: their performance is heavily constrained by the training data.…”
Section: Individual Tree Crown (Itc) Segmentationmentioning
confidence: 99%
“…R-CNN models analyze the image and extract self-features based on a predefined ground truth of each tree in the image. Therefore, CNN-based approaches show better performance for tree detection and classification than traditional segmentation algorithms [ 19 ]. However, it should be noted that these approaches require manual labeling (annotation) to generate the ground truth for each tree in the image, and the accuracy of the annotation process affects the overall accuracy of the models used to train the R-CNN model.…”
Section: Introductionmentioning
confidence: 99%