High-resolution image semantic segmentation network combining channel interaction spatial group attention and pyramid pooling

High spatial resolution remote sensing images contain rich information, it is therefore very important to study their semantic segmentation. Traditional machine learning methods appear low accuracy and efficiency when used for segmenting high-resolution remote sensing images. In recent years, the deep learning method has developed rapidly and has become the mainstream method of image semantic segmentation. Some scholars have introduced SegNet,Deeplabv3+, U-Net and other neural networks into remote sensing image semantic segmentation, but these networks have only limited effect in remote sensing image semantic segmentation. This paper improves the U-Net network for semantic segmentation of remote sensing images. Firstly, an improved convolutional attention module channel interaction and spatial group attention module(CISGAM) is embedded in the feature extraction stage of the U-Net network, so that the network can obtain more effective features; secondly, a residual module is used in the decoding layer to repla