A Lightweight Network for Building Extraction From Remote Sensing Images

Abstract

Building extraction is a fundamental research topic in remote sensing image interpretation. Convolutional neural network (CNN)-based building extraction algorithms have achieved high accuracy but require a large account of parameters and calculations, which hinders the practical application of these algorithms. To address the challenge, we propose a lightweight network (RSR-Net) for building extraction from remote sensing images. The network consists of three basic units with only a few parameters, and uses the idea of the fusion of shallow features and deep features, which is proposed by U-Net. Before features fusion, the squeeze-and-excitation (SE) module in RSR-Net assigned channel weights to these deep and shallow features. This operation can effectively reduce the influence of noise caused by shallow features in feature fusion, so as to improve the performance of the model. We estimated our network on datasets and achieved 88.32%, 71.58%, and 77.07% intersection-over-union (IoU) on datasets of aerial image and satellite image in Wuhan University (WHU) dataset, and the self-made building dataset of Guangzhou University Town, with only 2.81 M parameters and 6.91 G floating point operations (FLOPs). In addition, we propose a strategy combining target and background prediction, which makes RSR-Net achieve 0.37% improvement in IoU on WHU aerial image dataset. The effectiveness of RSR-Net is high. It showed that the proposed network is light and fast for the application of convolution neural network algorithm in practice.