Improved Fully Convolutional Network for the Detection of Built-up Areas in High Resolution SAR Images

Ding-Li Gao, Rong Zhang, Dixiu Xue

Abstract: High resolution synthetic aperture radar (SAR) images have been widely used in urban mapping and planning, and built-up areas in high resolution SAR images are the key point to the urban planning. Because of the high dynamics and multiplicative noise in high resolution SAR images, it is always difficult to detect built-up areas. To address this matter, we put forward an Improved Fully Convolutional Network (FCN) to detect built-up areas in high resolution SAR images. Our improved FCN model adopt a context network in order to expand the receptive fields of feature maps, and it is because that contextual fields of feature maps which are demonstrated plays a critical role in semantic segmentation performance. Besides, transfer learning is applied to improve the performance of our model because of the limited high resolution SAR images. Experiment results on the TerraSAR-X high resolution images of Beijing areas outperform the traditional methods, Convolutional Neural Networks (CNN) method and original FCN method.