Land Use Classification Using Improved U-Net in Remote Sensing Images of Urban and Rural Planning Monitoring

Abstract

Aiming at the problem of low accuracy and efficiency of existing land use classification methods for high-resolution remote sensing image segmentation, a land use classification method using improved U-Net in remote sensing images of urban and rural planning monitoring is proposed. First, taking the high-resolution remote sensing images of different remote sensing satellites as the data source, the remote sensing images in the data source are registered and cropped so that the pixels at the corresponding positions represent the same geographical location. Then, the encoder of the U-Net model is combined with the residual module to share the network parameters and avoid the degradation of the deep network. The dense connection module is integrated into the decoder to connect the shallow features with the deep features, so as to obtain new features and improve the feature utilization rate. Finally, the depthwise separable convolution is used to process the spatial and channel information of the convolution process separately to reduce the model parameters. Experiments show that the pixel accuracy, recall rate, precision rate, and average intersection-over-union ratio of the proposed land use classification method based on improved U-Net are 92.35%, 80.56%, 83.45%, and 86.75%, respectively, which are better than the compared methods. Therefore, the proposed method is proved to have good land use classification ability.