A Study of Remote Sensing Image Ground Segmentation on Deep Learning

Abstract

Remote sensing image segmentation plays an important role in the field of satellite image research. However, when dealing with the non-linear relationship with high spatial complexity, the accuracy of ground object segmentation is often low. Therefore, this paper proposes two methods of deep learning to improve the accuracy of ground object segmentation. This paper develops a method of integrated segmentation based on UNET integrated network. Several trained UNET network models are integrated with the weighted average method to get the UNET integrated network model. Then the CBAM (convolutional block attention module) is combined with UNET to get UNET attention model. The experimental results show that the segmentation accuracy of the improved UNET is higher.