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

Xu, Shicheng; Wang, Ruirui; Shi, Wei; Wang, Xiaoyan

doi:10.3390/f15010061

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Band combination is the simplest and commonly used method in multi-source remote sensing data fusion [6,27,29,66,67], and different combination methods showed different effects for different tree species. The identification accuracy of DGB and PCA-D was 75.5% and 76.2%, respectively, which indicates a good identification performance.…”

Section: Discussionmentioning

confidence: 99%

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Zhong,

Zhang,

Liu

et al. 2024

Forests

View full text Add to dashboard Cite

Automatic and accurate individual tree species identification is essential for the realization of smart forestry. Although existing studies have used unmanned aerial vehicle (UAV) remote sensing data for individual tree species identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic individual tree species identification using deep learning methods still require further exploration, especially in complex forest conditions. Therefore, this study proposed an improved YOLOv8 model for individual tree species identification using multisource remote sensing data under complex forest stand conditions. Firstly, the RGB and LiDAR data of natural coniferous and broad-leaved mixed forests under complex conditions in Northeast China were acquired via a UAV. Then, different spatial resolutions, scales, and band combinations of multisource remote sensing data were explored, based on the YOLOv8 model for tree species identification. Subsequently, the Attention Multi-level Fusion (AMF) Gather-and-Distribute (GD) YOLOv8 model was proposed, according to the characteristics of the multisource remote sensing forest data, in which the two branches of the AMF Net backbone were able to extract and fuse features from multisource remote sensing data sources separately. Meanwhile, the GD mechanism was introduced into the neck of the model, in order to fully utilize the extracted features of the main trunk and complete the identification of eight individual tree species in the study area. The results showed that the YOLOv8x model based on RGB images combined with current mainstream object detection algorithms achieved the highest mAP of 75.3%. When the spatial resolution was within 8 cm, the accuracy of individual tree species identification exhibited only a slight variation. However, the accuracy decreased significantly with the decrease of spatial resolution when the resolution was greater than 15 cm. The identification results of different YOLOv8 scales showed that x, l, and m scales could exhibit higher accuracy compared with other scales. The DGB and PCA-D band combinations were superior to other band combinations for individual tree identification, with mAP of 75.5% and 76.2%, respectively. The proposed AMF GD YOLOv8 model had a more significant improvement in tree species identification accuracy than a single remote sensing sources and band combinations data, with a mAP of 81.0%. The study results clarified the impact of spatial resolution on individual tree species identification and demonstrated the excellent performance of the proposed AMF GD YOLOv8 model in individual tree species identification, which provides a new solution and technical reference for forestry resource investigation combined multisource remote sensing data.

show abstract

Section: Discussionmentioning

confidence: 99%

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Zhong,

Zhang,

Liu

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

“…In the realm of CNN for tree species classification, the majority of research on tree species classification centers on identifying single tree species or individual species within forest stands. There is a relative scarcity of studies addressing the identification of complex forest structures and mixed forests with multiple tree species [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Convolutional Neural Network for Forest Stand Classification Based on Multi-Source Data Collaboration

Zhao,

Jing,

Zhang

et al. 2024

Forests

View full text Add to dashboard Cite

Accurate classification of forest stand is crucial for protection and management needs. However, forest stand classification remains a great challenge because of the high spectral and textural similarity of different tree species. Although existing studies have used multiple remote sensing data for forest identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic complex forest stand identification using deep learning methods still require further exploration. Therefore, this study proposed an object-oriented convolutional neural network (OCNN) classification method, leveraging data from Sentinel-2, RapidEye, and LiDAR to explore classification accuracy of using OCNN to identify complex forest stands. The two red edge bands of Sentinel-2 were fused with RapidEye, and canopy height information provided by LiDAR point cloud was added. The results showed that increasing the red edge bands and canopy height information were effective in improving forest stand classification accuracy, and OCNN performed better in feature extraction than traditional object-oriented classification methods, including SVM, DTC, MLC, and KNN. The evaluation indicators show that ResNet_18 convolutional neural network model in the OCNN performed the best, with a forest stand classification accuracy of up to 85.68%.

show abstract

Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework

Feng,

Li,

Wang

et al. 2024

Information Fusion

View full text Add to dashboard Cite

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

Cited by 4 publications

References 24 publications

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Individual Tree Species Identification for Complex Coniferous and Broad-Leaved Mixed Forests Based on Deep Learning Combined with UAV LiDAR Data and RGB Images

Object-Oriented Convolutional Neural Network for Forest Stand Classification Based on Multi-Source Data Collaboration

Security of target recognition for UAV forestry remote sensing based on multi-source data fusion transformer framework

Contact Info

Product

Resources

About