SAA-UNet:Remote Sensing Image Segmentation Based on Feature Information Fusion Network
Aiming at the problems of low accuracy and blurred edge segmentation of satellite-based remote sensing image segmentation,this paper proposes a high and low level feature information fusion network model.Based on the U-Net network model,the attention mechanism module is added in the encoder to obtain the low-level feature information,and the semantic embedding branch is used in the decoder to fuse the high-level information with the low-level information,and a module is constructed at the end of the encoder by using the mixed hole convolution with different hole expansion rates.To verify the effectiveness of the network,the WHDLD dataset and DeepGlobe-Road dataset are used as data sources,and the SAA-UNet model is compared with the current commonly used semantic segmentation network models.The experimental results show that the overall segmentation accuracy of SAA-UNet model is better than that of the comparison model,and the segmentation effect on small target features is better.The mean intersection ratio and category mean pixel accuracy in the WHDLD dataset are higher than that of the suboptimal model by 0.013 and 0.027,respectively.To verify the model generalization ability,the DeepGlobe-Road dataset is used for validation.The results show that SAA-UNet can effectively improve the segmentation accuracy of satellite-based remote sensing images.
deep learningsemantic segmentationhybrid null convolutionattention mechanismsemantic embedding branch
NETL
NSTL
万方数据
