Remote sensing image segmentation network based on attention mechanism feature fusion

Abstract

For remote sensing image segmentation projects, it is usually necessary to segment the boundary of ground objects finely, but the fitting of the boundary is a difficult problem in deep learning image segmentation. At the same time, for remote sensing images taken from high altitude, usually many large-scale targets in natural scenes will become small target objects in remote sensing images. To solve this problem, in order to improve the recognition effect of remote sensing image scene targets with huge scale differences, this paper combines UNet and FPN. This paper uses the framework of pytorch1.7.1 to establish the UNet + FPN model. Through the experimental results and visual analysis of building data sets and road data sets, the model in this paper has obvious advantages.