A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning

Abstract

Semantic segmentation of remote sensing images is an important issue in remote sensing tasks. Existing algorithms can extract information more accurately, but it is difficult to capture the contours of objects and further reveal the interaction information between different objects in the image. Therefore, a deep learning-based method for extracting building information from remote sensing images is proposed. First, the deep learning semantic segmentation model DeepLabv3+ and Mixconv2d are combined, and convolution kernels of different sizes are used for feature recognition. Then, the regularization method based on Rdrop Loss improves the accuracy and efficiency of contour capture for objects of different resolutions, and at the same time improves the consistency of dataset fitting. Finally, the proposed remote sensing image information extraction method is verified based on the self-built dataset. The experimental results show that the proposed algorithm can effectively improve the algorithm efficiency and result accuracy, and has good segmentation performance.