Abstract:

A method of building change detection in remote sensing image based on improved U-Net is proposed. The U-Net model is used to classify the image, and the problem of change detection is transformed into a two classification problem. Each pixel in the image is divided into a change class or a non change class,the change detection results are obtained according to the change class pixels. In order to improve the robustness of the convolution kernel and prevent over fitting, asymmetric convolution block is proposed to replace the standard convolution operation of the feature extraction part of U-Net network. Aiming at the problem that the background of the image in the change detection data set is complex and the change of the small target is easy to be missed, the attention mechanism is introduced into U-Net to restrain the learning of the model to the non-change type pixel features, strengthen the learning of the change type features, and extract the more suitable features. The experimental results show that after the introduction of asymmetric convolution block and attention mechanism, the F1 score of change detection is significantly improved.

Key words: building change detection, U-Net, asymmetric convolution block, attention mechanism

摘要：

提出了一种改进U-Net的遥感图像中建筑物变化检测方法，将变化检测问题转化为像素级二分类问题，利用U-Net模型对图像进行分类，把图像中的每个像素划分为变化类或非变化类，并根据变化类的像素得到建筑物的变化检测结果图。针对U-Net模型进行遥感图像中建筑物变化检测时，在训练中容易出现过拟合的现象，提出用非对称卷积块代替U-Net网络特征提取部分的标准卷积操作，增强卷积核的鲁棒性和网络的中心骨架，防止过拟合；针对变化检测数据集中图像背景复杂、小目标的变化情况容易被漏检的问题，提出在U-Net中引入注意力机制，抑制模型对非变化类像素特征的学习，加强对变化类特征的学习，提取到更适合的特征。实验结果表明，在引入非对称卷积块和注意力机制后，变化检测的F1分数有明显的提升。

关键词: 建筑物变化检测, U-Net, 非对称卷积块, 注意力机制