Land use information extraction from remote sensing images based on deep learning

Accurate land use information extraction plays an important role in land management, environmental monitoring, urban and rural planning and development and ecological assessment. High resolution remote sensing images have the characteristics of class similarity. Tradi¬tional methods of extracting ground object information from remote sensing images are limited to specific scenes and data sources, and have low segmentation accuracy and efficiency. In this paper, we shed the light to this research topic. To further improve the accuracy of land use information extraction, this paper takes HRNet(High-level Resolution Network) as the backbone network, which improves the performance of the network by fusing multi-scale feature map information through parallel work. Then, using the feature map information as input, the classification of each pixel is predicted through OCRNet network to achieve semantic segmentation of the image. The results show that: OCRNet-18 segmentation model is superior to other deep learning models in terms of PA, M IoU and Kappa coefficients, and the accuracy of PA (total), Kappa coefficient and M IoU are 0.913, 0.866, and 0.644 respectively.