Research on High-resolution Image Feature Segmentation based on Improved DeepLabV3+ Algorithm
Aiming at the common shortcomings of the research on high-resolution remote sensing im-age ground object segmentation,such as complex computation,low segmentation accuracy and large holes,this paper proposes DeepLabV3+ network model with dual attention mechanism.This model uses lightweight MobilenetV2 as the backbone network,and fully considers three aspects of network parameter setting,edge extraction optimization and performance,so as to obtain dense context infor-mation.The model designed in this paper is applied to the high-resolution remote sensing image dataset of Xunwu county for verification.The results show that the DeepLabV3+ network model with dual attention and resourceful achieves good segmentation accuracy for eight ground objects,espe-cially for the segmentation of gardens,water bodies and roads,with the segmentation accuracy up to 92%,90%and 96%.This study provides a scientific reference for high-resolution image feature segmentation and how to make up for the defects of basic DeepLavV3+.
国家科技期刊平台
NSTL
万方数据
维普
