Remote sensing image segmentation based on ICNet network and lightweight structure

In remote sensing image processing, image segmentation requires not only the accuracy of segmentation, but also requires the lightweight of the network model as a way to improve the training speed to achieve faster response time. Therefore, in the pursuit of efficient real-time application requirements, this paper improves the lightweight network structure ICNet. Firstly, the efficient channel attention (ECA) module is added to ICNet, and the ECA module improves the feature extraction capability and ensures the simplicity of the model through the weighting operation of the key channel information. Then the joint pyramid upsampling (JPU) module is also introduced, which is integrated into the original ICNet's upper, middle, and lower branching structure for processing feature information. In the subsequent experiments, the proposed EJICNet network structure is trained and evaluated in depth, and the experimental results clearly show that EJICNet significantly reduces the computational complexity while maintaining a higher segmentation accuracy compared to the existing network structure. This proves that the optimisation method proposed in this study balances efficiency and accuracy, and satisfies the network's need for real-time performance.