本文基于融合VDVI指数后的无人机RGB影像，利用深度学习技术进行互花

In this paper, based on the UAV RGB images fused with the VDVI index, the deep learning technology was used to monitor Spartina alterniflora, and the overall accuracy and kappa coefficient were 98.1% and 0.960, respectively. The accurate extraction at the block scale is feasible, and it is helpful for monitoring the early fragmentation distribution of Spartina alterniflora invasion.

Based on the UAV RGB image fused with vdvi index, this paper uses deep learning technology to monitor Spartina alterniflora, and the overall accuracy and kappa coefficient are 98.1% and 0.960 respectively. Therefore, this method is feasible to accurately extract Spartina alterniflora in coastal wetland on the patch scale, and it is helpful for the monitoring of Spartina alterniflora distribution in the early stage of invasion.

Based on the UAV RGB images fused with VDVI index, this paper uses deep learning technology to monitor Spartina alterniflora, and the overall accuracy and kappa coefficient are 98.1% and 0.960 respectively. Therefore, it is feasible to accurately extract Spartina alterniflora on the patch scale in coastal wetlands by using this method, and it is helpful to monitor the patchy distribution of Spartina alterniflora in the early stage of invasion.