Automatic Building Segmentation of Aerial Imagery Using Multi-Constraint Fully Convolutional Networks

• Main Authors: Guangming Wu, Xiaowei Shao, Zhiling Guo, Qi Chen, Wei Yuan, Xiaodan Shi, Yongwei Xu, Ryosuke Shibasaki
• Format: Article
• Language: English
• Published: MDPI AG 2018-03-01
• Series: Remote Sensing
• Subjects: aerial imagery; building detection; convolutional neural network; multi-constraint fully convolutional networks; feature pyramid
• Online Access: http://www.mdpi.com/2072-4292/10/3/407

Automatic building segmentation from aerial imagery is an important and challenging task because of the variety of backgrounds, building textures and imaging conditions. Currently, research using variant types of fully convolutional networks (FCNs) has largely improved the performance of this task. However, pursuing more accurate segmentation results is still critical for further applications such as automatic mapping. In this study, a multi-constraint fully convolutional network (MC–FCN) model is proposed to perform end-to-end building segmentation. Our MC–FCN model consists of a bottom-up/top-down fully convolutional architecture and multi-constraints that are computed between the binary cross entropy of prediction and the corresponding ground truth. Since more constraints are applied to optimize the parameters of the intermediate layers, the multi-scale feature representation of the model is further enhanced, and hence higher performance can be achieved. The experiments on a very-high-resolution aerial image dataset covering 18 km 2 and more than 17,000 buildings indicate that our method performs well in the building segmentation task. The proposed MC–FCN method significantly outperforms the classic FCN method and the adaptive boosting method using features extracted by the histogram of oriented gradients. Compared with the state-of-the-art U–Net model, MC–FCN gains 3.2% (0.833 vs. 0.807) and 2.2% (0.893 vs. 0.874) relative improvements of Jaccard index and kappa coefficient with the cost of only 1.8% increment of the model-training time. In addition, the sensitivity analysis demonstrates that constraints at different positions have inconsistent impact on the performance of the MC–FCN.