Classification of land use types in multispectral remote sensing images based on deep learning
In this paper,a land use classification network(MSNet)for multispectral remote sensing images is proposed,which has the characteristics of visible light band and infrared band.Based on ResNet-50 as the backbone network,different stages of the network are optimized and improved.Comparing MSNet with classical image segmentation model UNet,the results show that MSNet is better,and the evaluation indexes of accuracy,F1 score and recall rate are all in the range of(0.8,1).Experiments show that MSNet can make full use of multispectral features and improve the classification accuracy of remote sensing images.
deep learningland use typemultispectral informationremote sensing image
万方数据
维普
