关键词: 无人机遥感, 互花米草, 卷积神经网络, 零散斑块

Abstract: The invasion of Spartina alterniflora has caused great losses to ecosystem diversity in China. How to accurately identify Spartina alterniflora in scattered patches is important for early monitoring and early warning. In this paper, lowaltitude UAV remote sensing technology was used to study Spartina alterniflora in spring mangroves in Beihai area of Guangxi. Five convolutional neural networks including AlexNet, VGG16, GoogleNet, ResNet50 and EfficientNetB0 were used. Model training, validation and testing were carried out on image data sets containing Spartina alterniflora, mangrove and other ground object backgrounds respectively, then the cultivated five network models were used to identify and mark Spartina alterniflora and ground object backgrounds in the whole experiment area, obtaining the distribution map of Spartina alterniflora. The comprehensive quantitative evaluation results based on confusion matrix and operation time showed that ResNet50 network model was superior to the other four network models in general, with the highest recognition accuracy of 96.96%, and the test set took only 5.47 s. The identification results were compared with the actual distribution of Spartina alterniflora, and the identification results of ResNet50 network model basically coincided with the actual distribution of Spartina alterniflora.

Key words: UAV remote sensing, Spartina alterniflora, convolutional neural network, scattered patches