The Detection of Built-up Areas in High-Resolution SAR Images Based on Deep Neural Networks

Yunfei Wu, Rong Zhang, Yue Li

Abstract: The detection of built-up areas is an important task for high-resolution Synthetic Aperture Radar (SAR) applications, such as urban planning and environment evaluation. In this paper, we proposed a deep neural network based on convolutional neural networks for the detection of built-up areas in SAR images. Since lables of neighboring pixels have strong correlation in SAR images, informations on labels of neighboring pixels could help making better prediction. In addition, built-up areas in SAR images possess various scales, multiscale representations is critical for the detection of built-up areas. Based on above observations, we introduce the structured prediction into our network, where a network classifies multiple pixels simultaneously. Meanwhile, we attempt to adopt multi-level features in our network. Experiments on TerraSAR-X high resolution SAR images over Beijing show that our method outperforms traditional methods and CNNs methods.