基于特征压缩激活Unet网络的建筑物提取

自动提取城市建筑物对城市规划、防灾避险等行业应用具有重要意义,当前利用高空间分辨率遥感影像进行建筑物提取的卷积神经网络在网络结构和损失函数上都存在提升的空间。本研究提出一种卷积神经网络SE-Unet,以U-Net网络结构为基础,在编码器内使用特征压缩激活模块增加网络特征学习能力,在解码器中复用编码器中相应尺度的特征实现空间信息的恢复;并使用dice和交叉熵函数复合的损失函数进行训练,减轻了建筑物提取任务中的样本不平衡问题。实验采用了Massachusetts建筑物数据集,和SegNet、LinkNet、U-Net等模型进行对比,实验中SE-Unet在准确度、召回率、F1分数和总体精度 4项精度指标中表现最优,分别达到0.8704、0.8496、0.8599、0.9472,在测试影像中对大小各异和形状不规则的建筑物具有更好的识别效果。

Building Extraction based on SE-Unet

Automatic extraction of urban buildings has great importance in applications like urban planning and disaster prevention. In this regard, high-resolution remote sensing imagery contain sufficient information and are ideal data for precise extraction. Traditional approaches (excluding visual interpretation) demand researchers to manually design features to describe buildings and distinguishing them from other objects. Unfortunately, the complexity in high-resolution imagery makes these features fragile due to the change of sensors, imaging conditions, and locations. Recently, the convolutional neural networks, which succeeded in many visual applications including image segmentation, were used to extract buildings in high spatial resolution remote sensing imagery and achieved desirable results. However, convolutional neural networks still have much to improve regarding especially network architecture and loss functions. This paper proposed a convolutional neural network SE-Unet. It is based on U-Net architecture and employs squeeze-and-excitation modules in its encoder. The squeeze-and-excitation modules activate useful features and deactivate useless features in an adaptively weighted manner, which can remarkably increase network capacity with only a few extra parameters and memory cost. The decoder of SE-Unet concatenates corresponding features in the encoder to recover spatial information, as the U-Net does. Dice and cross-entropy loss function was applied to train the network and successfully alleviated the sample imbalance problem in building extraction. All experiments were performed on the Massachusetts building dataset for evaluation. Comparing to SegNet, LinkNet, U-Net, and other networks, SE-Unet showed the best results in all evaluation metrics, achieving 0.8704, 0.8496, 0.8599, and 0.9472 in terms of precision, recall, F1-score, and overall accuracy, respectively. Also, SE-Unet presented even better precision in extracting buildings that vary in size and shape. Our findings prove that squeeze-and-excitation modules can effectively strengthen network capability, and that dice and cross-entropy loss function can be useful in other sample imbalanced situations that involve high-resolution remote sensing imagery.