Application Research of Deep Learning Technology in Natural Landscape Animation Design

Abstract

Due to the limitation of technology and cost, the animation design of natural landscape in the past was often dealt with relative simplicity. With the increasing level of audience appreciation and the continuous development of animation technology, new requirements are put forward for the design of natural landscape animation. In order to make the animation design effect of natural landscape more real, the synthetic aperture radar image is firstly analyzed to obtain the location of mountains, farmland, rivers, villages, roads, and buildings. Considering the superiority of U-Net network in image semantic segmentation, this paper constructs a semantic segmentation model based on U-Net structure. In this model, dense connection module is introduced in downsampling, and spatial void pyramid structure is introduced in upsampling to retain more image features, to achieve accurate segmentation of satellite images. Experimental results show that the proposed algorithm has higher segmentation accuracy than other algorithms. After accurate classification of natural scene images, it can provide a guarantee for designing more real natural landscape animation design effects.