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基于深度迁移学习的无人机高分影像树种分类与制图

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

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摘要

提出一种基于深度迁移学习的无人机高分影像树种分类与制图方法。利用ImageNet上训练的大型卷积神经网络提取树种影像特征,采用全局平均池化压缩树种影像特征,使用简单线性迭代聚类生成超像素,以超像素为最小分类单元,生成树种专题地图。实验结果表明,在类间差距小、类内差距大的情况下,与小型卷积神经网络相比,本文方法收敛更快,总体精度和Kappa系数分别提高了9.04%和0.1547,超像素树种制图边界更加精确。

Abstract

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.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3788/lop56.072801

所属栏目:遥感与传感器

基金项目:国家重点研发计划(2016YFC0502704)、国家自然科学基金(41601455)、安徽高校省级自然科学研究重点项目(KJ2016A531)

收稿日期:2018-10-08

修改稿日期:2018-10-17

网络出版日期:2018-10-22

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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

联系人作者:温小荣(wenxiu_teng@163.com)

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引用该论文

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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