基于密集卷积网络(DenseNet)的无人机多时相RGB图像树种识别

IF 1.3 Q3 REMOTE SENSING
Sowmya Natesan, C. Armenakis, U. Vepakomma
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引用次数: 28

摘要

树木个体层面的树种识别对森林运营和管理至关重要,但其自动化绘图仍然具有挑战性。新兴技术,如无人驾驶飞行器(UAV)的高分辨率图像,现在正成为每个林业工作者监测工具包的一部分,有可能提供一种更好地描述树冠特征的解决方案。为了满足这一需求,我们开发了一种基于深度卷积神经网络(CNN)的方法,在单个树木级别对森林树种进行分类,该方法使用从安装在无人机平台上的消费级相机获取的高分辨率RGB图像。这项工作探索了密集卷积网络(DenseNet)对加拿大东部常见的经济针叶树种进行分类的能力。该网络使用在不同采集参数下捕获的多时相图像进行训练,包括季节、时间、照明和角度变化。在加拿大安大略省的一片混合树林中,使用不同的图像对该模型进行了验证,结果显示,在区分五种主要针叶树时,分类准确率超过84%。即使使用在不同季节和时间拍摄的图像,以及不同的照明和角度,该模型也保持高度鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV
Tree species identification at the individual tree level is crucial for forest operations and management, yet its automated mapping remains challenging. Emerging technology, such as the high-resolution imagery from unmanned aerial vehicles (UAV) that is now becoming part of every forester’s surveillance kit, can potentially provide a solution to better characterize the tree canopy. To address this need, we have developed an approach based on a deep Convolutional Neural Network (CNN) to classify forest tree species at the individual tree-level that uses high-resolution RGB images acquired from a consumer-grade camera mounted on a UAV platform. This work explores the ability of the Dense Convolutional Network (DenseNet) to classify commonly available economic coniferous tree species in eastern Canada. The network was trained using multitemporal images captured under varying acquisition parameters to include seasonal, temporal, illumination, and angular variability. Validation of this model using distinct images over a mixed-wood forest in Ontario, Canada, showed over 84% classification accuracy in distinguishing five predominant species of coniferous trees. The model remains highly robust even when using images taken during different seasons and times, and with varying illumination and angles.
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
2
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