Remote Sensing Urban Green Space Layout and Site Selection Based on Lightweight Expansion Convolutional Method

Abstract

With the improvement of remote sensing image resolution, remote sensing image scene classification has become a major difficulty in the research of remote sensing Urban green space spatial layout and site selection. Complex data and network structure affect the processing effect of the traditional Convolutional neural network model, so it is particularly important to design a more efficient Convolutional neural network. This research will first expand the convolution design of the Convolutional neural network to improve the scope of model recognition, then select two methods of structural pruning and separable knowledge distillation for lightweight processing of the model, and finally introduce relevant models for comparative experiments to verify the lightweight effect of the model. The experimental results show that the global average accuracy of the lightweight model based on structural pruning is 95.5%, and the Kappa coefficient value is 0.947; The global classification accuracy of the lightweight model based on knowledge distillation reaches 95.60%, with a Kappa coefficient value of 0.939. It only uses 38.419MB of storage space to recognize a remote sensing image, 4698352 model parameters, and 1397527639 floating-point operations per second. The results show that the expansion convolution and network pruning methods improve the classification performance of the Convolutional neural network, improve the accuracy of the model, and the knowledge distillation method has a better effect in reducing the complexity and making up for the loss of the classification performance of the network model.

Keywords

• Dilated convolution
• structural pruning
• knowledge distillation
• convolutional neural network
• remote sensing image classification