Vegetation Classification of UAV Multispectral Remote Sensing Images Based on Deep Learning

Abstract

With the aim of providing a reliable prediction model for vegetation detection and ground classification, a multispectral dataset was produced for semantic segmentation, which utilizes multispectral UAV images and is based on a combination of support vector machines and manual annotation. Also, a 3D-UNet model is proposed on which the dataset is trained and experiments show that the model has achieved 89.9 % prediction for the validation set.