The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.

中文翻译：

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基于高原山区无人机影像的烟草种植区深度语义分割

高原山区的烟草具有散播，生长不均匀和农作物混种/间种的特点。使用面向对象的图像分析方法来准确提取烟草种植区域很难提取有效特征。为此，本文依靠深度学习功能自学习的优势。提出了一种基于深度语义分割模型的高原山区无人机图像烟草提取区域的精确提取方法。首先，利用Labelme建立了烟草语义分割数据集。DeeplabV3 +，PSPNet，SegNet和U-Net的四个深度语义分割模型用于训练数据集中的样本数据。其中，为了减少模型训练时间，MobileNet系列轻量级网络用于替换四种网络模型中的原始骨干网。最后，通过训练有素的网络对预测图像进行语义分割，并使用均值交会（mIoU）评估准确性。实验结果表明，使用DeeplabV3 +，PSPNet，SegNet和U-Net对71个场景预测图像进行语义分割，得到的mIoU分别为0.9436、0.9118、0.9392和0.9473，语义分割的准确性较高。 。验证了深度语义分割方法从无人机遥感图像中提取烟叶表面的可行性，该研究方法可为后续自动提取烟叶面积提供参考。