A Lightweight Model of VGG-U-Net for Remote Sensing Image Classification

Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of the model to 98%. The memory size of the model is 3.4 MB. At the same time, The classification and convergence speed of the model are greatly improved. We simultaneously take the remote sensing scene image of 64 × 64 as input into the designed model. As the accuracy of the model is 97%, it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy. Therefore, the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.