Buildings Extraction from Remote Sensing Data Using Deep Learning Method Based on Improved U-Net Network

Abstract

Due to the different shapes of buildings and the cross-distribution with various surface types around them, it is difficult to extract buildings in high precision using traditional classification methods. The deep learning method based on neural network can mine useful information of remote sensing image in depth and improve the accuracy of building recognition. However, the application of neural network in building extraction is limited because of the large number of parameters involved and the large demand for training samples. In order to improve the accuracy of building extraction in remote sensing images by using deep learning method, identity skip connection is inserted into U-net network for samples training, which effectively reduces the number of parameters, significantly reduces the size of the model, and avoids the gradient explosion caused by the deepening of the number of layers, and obviously improves the accuracy of segmentation. By comparing the results of different layers, it is shown that with the deepening of layers, the accuracy increases.