Segmentation of High Spatial Resolution Remote Sensing Image based On U-Net Convolutional Networks

Abstract

With the rapid development of deep learning in recent years, the field of remote sensing image processing has gradually started to use some deep learning algorithms to achieve intelligent and fast processing of images, and the results have improved to a certain extent compared to traditional methods. The U-Net convolutional neural network was proposed used in medical image segmentation in 2015. Based on the previous work, we transferred the U-Net to remote sensing image segmentation to realize the pixel level semantic segmentation of remote sensing image end-to-end. Through U-Net training and learning on GF-2 remote sensing image, the overall accuracy of training sets is 93.83%, while the overall accuracy of the test data is 82.27%, the kappa coefficient is 0.7721, and the Mean intersection Over Union (MiOU) is 0.6405. The results showed that the experiment has high segmentation accuracy and generalization ability.