HRNet- and PSPNet-based multiband semantic segmentation of remote sensing images

High-resolution remote sensing images have become mainstream remote sensing data, but there is an obvious "salt and pepper phenomenon" in the existing semantic segmentation methods of high-resolution remote sensing images. The purpose of this paper is to propose an improved deep convolutional neural network based on HRNet and PSPNet to segment and realize deep scene analysis and improve the pixel-level semantic segmentation representation of high-resolution remote sensing images. Based on hierarchical multiscale segmentation technology research, the main method is multiband segmentation; the vegetation, buildings, roads, waters and bare land rule sets in the experimental area are established, the classification is extracted, and the category is labeled at each pixel in the image. Using the image classification network structure, different levels of feature vectors can be used to meet the judgment requirements. The HRNet and PSPNet algorithms are used to analyze the scene and obtain the category labels of all pixels in an image. Experiments have shown that artificial intelligence uses the pyramid pooling module in the classification and recognition of CCF satellite images. In the context of integrating different regions, PSPNet affects the region segmentation accuracy. FCN, DeepLab and PSPNet are now the best methods and achieve 98% accuracy. However, the PSPNet object recognition algorithm has better advantages in specific areas. Experiments show that this method has high segmentation accuracy and good generalization ability and can be used in practical engineering.