Remote sensing image semantic segmentation network based on ENet

The current image semantic segmentation methods cannot meet the requirements of high precision and high speed for remote sensing image analysis. The ENet network model builds a semantic segmentation network, which has the characteristics of few network parameters and fast operation speed. The attention mechanism module is integrated with the ENet network model, which can deeply mine image features in remote sensing datasets and ensure the accuracy of semantic segmentation. The author combines the ENet network with the attention mechanism to construct a new semantic segmentation network model. The model first constructed a remote sensing image semantic segmentation network model based on the ENet network, and simplified the model to further improve the speed of image segmentation and recognition. Then, the attention mechanism module is fused with the ENet network model, which can conduct deep and orderly mining of the image features of the remote sensing image data set. It can meet the accuracy requirements of remote sensing image semantic analysis. Simulations are performed based on three general datasets, and the experimental results show high accuracy and high speed.