Automatic Building Segmentation of Aerial Imagery UsingMulti-Constraint Fully Convolutional Networks
Automatic Building Segmentation of Aerial Imagery UsingMulti-Constraint Fully Convolutional Networks
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DOI：
10.3390/rs10030407
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发表时间：
2018-03-01
期刊：
影响因子：
5
通讯作者：
Shibasaki, Ryosuke
中科院分区：
文献类型：
--
作者：
Wu, Guangming;Shao, Xiaowei;Shibasaki, Ryosuke
航拍图像中建筑物的自动分割是一项重要而具有挑战性的任务，因为建筑物的背景、纹理和成像条件多种多样。目前，使用不同类型的全卷积网络(FCN)的研究已经在很大程度上提高了这项任务的性能。然而，追求更准确的分割结果对于进一步的应用仍然至关重要，例如自动映射。本文提出了一种多约束完全卷积网络(MC-FCN)模型来进行端到端的建筑物分割。我们的MC-FCN模型由自下而上/自上而下的全卷积结构和多个约束组成，这些约束是在预测的二进制交叉熵和相应的基本事实之间计算的。由于应用了更多的约束来优化中间层的参数，进一步增强了模型的多尺度特征表示，因此可以获得更高的性能。在覆盖18公里(2)、超过17,000栋建筑物的高分辨率航空影像数据集上的实验表明，该方法在建筑物分割任务中取得了良好的效果。本文提出的MC-FCN方法明显优于经典的FCN方法和利用方向梯度直方图提取的特征的自适应Boosting方法。与最新的U-Net模型相比，MC-FCN的Jaccard指数和Kappa系数分别提高了3.2%(0.833比0.807)和2.2%(0.893比0.874)，而模型训练时间仅增加了1.8%。另外，灵敏度分析表明，不同位置的约束对MC-FCN的性能影响不一致。
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.