Abstract

This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology.

摘要

这项研究提出了一种方法，该方法使用无人飞行器（UAV）收集的高分辨率遥感图像，并结合MobileNet和Faster R-CNN来检测患病的松树。MobileNet用于删除背景以减少背景信息的干扰。采用更快的R-CNN来区分病树和健康松树。由于可用的无人机图像数量不足，训练样本的数量有所增加。实验结果表明，该方法优于传统的机器学习方法，如支持向量机和AdaBoost；以及DCNN的方法，如Alexnet，Inception和Faster R-CNN。通过样本扩展和背景去除，该方法利用深度学习技术实现了无人机图像中病态松树的有效检测。