The oasis-desert ecotone plant community is a protective barrier for an oasis. With the continuous expansion of oasis ecosystems and gradual increases in the intensity of human activities, the degradation of plant communities in oasis-desert ecotones has become increasingly prominent. Timely and accurate detection of such degradation is a prerequisite for vegetation restoration. Currently, vegetation information extraction has been primarily based on an analysis of spectral features; however, the vegetation coverage area and the soil background are easily confused. In addition, conventional supervised classification methods have a strong dependence on the training samples, and this technique can fail due to the complicated image processing procedure, relatively lower recognition ability, and optimal threshold determination for a multi-temporal image. In this study, the aim was to accurately extract the plant community features, distribution area, and the image background using two automatic recognition algorithm models known as the convolution neural network (CNN)-based VGG16 and VGG19 models. These models were used to investigate an oasis-desert ecotone in an arid area using an unmanned aerial vehicle (UAV) remote-sensing image. Additionally, the impacts of a change in the training sample size on the automatic classification accuracy of the models were evaluated. The results showed that the size of the training samples has a significant impact on the classification accuracy, and with an increase in the sample sizes, the generalization ability of the models gradually improved. The modeling accuracy of the VGG16 and VGG19 increased from 88.25% and 95.25% to 88.50% and 96.73%, respectively. The classification accuracy of the VGG16 model varied from 79.6 to 93.8%, and the classification accuracy of VGG19 model varied from 82.3 to 95.6%. The size of the training samples was 300, so both models presented the best classification results. Compared with the conventional supervised classification methods, the deep learning algorithm-based models yielded significantly higher classification accuracies. These models can provide technical support for the realization of the unsupervised automatic classification of oasis-desert ecotone plant communities in arid areas.

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基于深度学习的无人机低空遥感数据对绿洲荒漠过渡带植物群落的快速识别

绿洲荒漠过渡带植物群落是绿洲的保护屏障。随着绿洲生态系统的不断扩展和人类活动强度的逐步提高，绿洲-荒漠过渡带中植物群落的退化日益突出。及时准确地检测出这种退化是恢复植被的前提。目前，植被信息提取主要基于对光谱特征的分析。但是，植被覆盖区和土壤背景容易混淆。此外，传统的监督分类方法对训练样本有很强的依赖性，并且由于图像处理过程复杂，识别能力相对较低，多时相图像的最佳阈值确定。在这项研究中，目的是使用两种基于基于卷积神经网络（CNN）的VGG16和VGG19模型的自动识别算法模型来准确提取植物群落特征，分布区域和图像背景。这些模型用于使用无人飞行器（UAV）遥感图像研究干旱地区的绿洲荒漠过渡带。此外，评估了训练样本大小的变化对模型的自动分类准确性的影响。结果表明，训练样本的大小对分类精度有重要影响，并且随着样本大小的增加，模型的泛化能力逐渐提高。VGG16和VGG19的建模精度分别从88.25％和95.25％提高到88.50％和96.73％。VGG16模型的分类精度在79.6％到93.8％之间，VGG19模型的分类精度在82.3％到95.6％之间。训练样本的大小为300，因此这两个模型都表现出最好的分类结果。与传统的监督分类方法相比，基于深度学习算法的模型产生了更高的分类精度。这些模型可以为实现干旱地区绿洲-荒漠过渡带植物群落的无监督自动分类提供技术支持。VGG19模型的分类精度在82.3％至95.6％之间。训练样本的大小为300，因此这两个模型都表现出最好的分类结果。与传统的监督分类方法相比，基于深度学习算法的模型产生了更高的分类精度。这些模型可以为实现干旱地区绿洲-荒漠过渡带植物群落的无监督自动分类提供技术支持。VGG19模型的分类精度在82.3％至95.6％之间。训练样本的大小为300，因此这两个模型都表现出最好的分类结果。与传统的监督分类方法相比，基于深度学习算法的模型产生了更高的分类精度。这些模型可以为实现干旱地区绿洲-荒漠过渡带植物群落的无监督自动分类提供技术支持。