Abstract: Multi-spectral image segmentation is an important basic link in remote sensing image interpretation.SAR remote sensing images contain complex object information.Traditional segmentation methods have problems such as time-consuming and low efficiency,which limits the application of traditional image segmentation methods.In recent years,the application of deep learning algorithms in the direction of computer vision has achieved good results.Aiming at the problem of semantic segmentation of multi-spectral remote sensing images,deep learning semantic segmentation methods are used to achieve high-performance segmentation of remote sensing images.In the U-Net network structure Above,add activation layer,dropout layer,convolutional la-yer,and propose a deep convolutional neural network optimized based on U-Net.On the basis of a small amount of data set,it realizes for rapid detection of buildings and rivers,the overall segmentation accuracy rate reaches 94.6%.The comparison test results with U-Net and SegNet show that the segmentation accuracy of the method used in this paper is better than that of U-Net and SegNet.Compared with U-Net and SegNet,it has increased by a minimum of 2.5％ and 5.8％,respectively.

Key words: Deep learning, Multispectral, Remote sensing image, SAR, Semantic segmentation, U-Net