Land Use Classification Method of Remote Sensing Images for Urban and Rural Planning Monitoring Using Deep Learning

Abstract

Aiming at the problems that most existing segmentation methods are difficult to deal with the imbalance of remote sensing image distribution and the overlap of segmentation target edges, a land use classification method of remote sensing image for urban and rural planning monitoring based on deep learning is proposed. Firstly, the U-Net is improved by pooling index upsampling and dimension superposition. The improvement can not only extract high-level abstract features but also extract low-level detail features, so as to reduce the loss of image edge information in the process of deconvolution. Then, the batch normalization and scaling exponential linear unit (SeLU) are used to improve the U-Net model. Finally, the improved U-Net model is applied to the classification of remote sensing images of land use types to realize dynamic monitoring. The experimental analysis of the proposed method based on TensorFlow deep learning framework shows that its total accuracy exceeds 94%. The segmentation effect of land use types in remote sensing images is good.