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Tree Species Classification and Mapping Based on Deep Transfer Learning with Unmanned Aerial Vehicle High Resolution Images

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A tree species classification and mapping method is proposed based on the deep transfer learning with unmanned aerial vehicle high resolution images. The image features of tree species are extracted using a large convolution neural network trained on ImageNet. The features of tree species images are compressed by the global average pooling. A simple linear iterative clustering method is used to generate the super-pixel, which are used as the minimum classification unit to generate tree species maps. The experimental results show that the proposed method can accelerate the convergence of the training process. The overall accuracy and Kappa coefficient are increased by 9.04% and 0.1547, respectively, compared with the small convolutional neural network method in the case of small inter-class gap and the large intra-class gap, and the boundary of the super-pixel tree mapping is more accurate.









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滕文秀:南京林业大学南方现代林业协同创新中心, 江苏 南京, 210037南京林业大学林学院, 江苏 南京, 210037
温小荣:南京林业大学南方现代林业协同创新中心, 江苏 南京, 210037南京林业大学林学院, 江苏 南京, 210037
王妮:滁州学院地理信息与旅游学院, 安徽 滁州 239000安徽省地理信息智能感知与服务工程实验室, 安徽 滁州 239000
施慧慧:滁州学院地理信息与旅游学院, 安徽 滁州 239000


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Teng Wenxiu,Wen Xiaorong,Wang Ni,Shi Huihui. Tree Species Classification and Mapping Based on Deep Transfer Learning with Unmanned Aerial Vehicle High Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(7): 072801

滕文秀,温小荣,王妮,施慧慧. 基于深度迁移学习的无人机高分影像树种分类与制图[J]. 激光与光电子学进展, 2019, 56(7): 072801

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