ABSTRACT Forest roads are important features of the environment that have significant impacts on wildlife habitats. The U-net and U-shaped Fully Convoluted Network (FCN) deep-learning methods have demonstrated great potential in successfully extracting paved roads in urban environments; however, they require a large amount of pixel-based training samples, which is resource-consuming. During this study, a convolutional neural network (CNN) aided method for forest road identification and extraction was developed. The algorithm utilized the multivariate Gaussian and Laplacian of Gaussian (LoG) filters and VGG 16 on high spatial resolution multispectral imagery to extract both primary road and secondary roads in forested areas. It was tested on imagery over two areas in the Hearst forest located in central Ontario, Canada. Based on validation against manually digitized roads, over 74% of the roads from both test areas were successfully extracted.

中文翻译：

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通过卷积神经网络辅助方法提取林道

摘要 森林道路是环境的重要特征，对野生动物栖息地具有重大影响。U-net 和 U 形全卷积网络 (FCN) 深度学习方法在成功提取城市环境中的铺砌道路方面显示出巨大潜力；然而，它们需要大量基于像素的训练样本，这很消耗资源。在这项研究中，开发了一种用于森林道路识别和提取的卷积神经网络 (CNN) 辅助方法。该算法在高空间分辨率多光谱图像上利用多元高斯和拉普拉斯高斯 (LoG) 滤波器和 VGG 16 来提取林区的主要道路和次要道路。它在位于加拿大安大略省中部赫斯特森林的两个区域的图像上进行了测试。