An Improved Fully Convolutional Network for Learning Rich Building Features

Shuang Wang, Ligang Zhou, Pei He, Dou Quan, Qing Zhao, Xuefeng Liang, Biao Hou

Abstract: Many efficient approaches are proposed to detect building in remote sensing images. In this paper, in order to learning rich building features better, we propose a full convolutional network with dense connection. There contributions are made: 1) To strengthen feature propagation, an improved dense network is introduced to the full convolution network. 2) We have designed top-down short connections to facilitate the fusion of high and low feature information. 3) In addition, we add the weighted cross entropy edge loss function to make the network pay more attention to building edge in detail. Experiments show that the proposed method achieves excellent performance on the remote sensing image data taken by the QuickBird satellite.