基于对象方法的VGG-16网络树种检测

M. Rezaee, Yun Zhang, Rakesh K. Mishra, Fei Tong, Hengjian Tong
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引用次数: 18

摘要

获取森林林分的信息,如单个树种,对监测森林至关重要。迄今为止,这些信息是由使用机载或无人驾驶飞行器(UAV)的人工口译员评估的,这是耗时/成本消耗的。最近在遥感图像采集方面取得的进展,如WorldView-3,将空间分辨率提高到30厘米,光谱分辨率提高到16个波段。这一进展大大增加了单个树种检测(ITSD)的潜力。为了使用单源Worldview-3图像,我们提出的方法首先对图像进行分割以描绘树木,然后使用VGG-16网络进行树木检测。我们开发了一个管道,利用所有8个可见-近红外波段的信息馈送深度CNN网络,并对其进行训练。结果与两种最先进的集成分类器即随机森林(RF)和梯度增强(GB)进行了比较。结果表明,VGG-16的精度达到92.13%,优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using a VGG-16 Network for Individual Tree Species Detection with an Object-Based Approach
Acquiring information about forest stands such as individual tree species is crucial for monitoring forests. To date, such information is assessed by human interpreters using airborne or an Unmanned Aerial Vehicle (UAV), which is time/cost consuming. The recent advancement in remote sensing image acquisition, such as WorldView-3, has increased the spatial resolution up to 30 cm and spectral resolution up to 16 bands. This advancement has significantly increased the potential for Individual Tree Species Detection (ITSD). In order to use the single source Worldview-3 images, our proposed method first segments the image to delineate trees, and then detects trees using a VGG-16 network. We developed a pipeline for feeding the deep CNN network using the information from all the 8 visible-near infrareds' bands and trained it. The result is compared with two state-of-the-art ensemble classifiers namely Random Forest (RF) and Gradient Boosting (GB). Results demonstrate that the VGG-16 outperforms all the other methods reaching an accuracy of about 92.13%.
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