Deep learning based change detection has brought a significant improvement in the accuracy and efficiency when compared with conventional machine learning methods. However, the issues of the lack of differential information and the diversity of the scale features of artificial objects are crucial barriers to the application of building change detection algorithms. A novel deep learning based approach named the high-resolution feature difference attention network (HDANet) is proposed in this work to solve these issues. HDANet can handle the change characteristics well, due to the Siamese network structure. To tackle the loss of the spatial features of buildings caused by the multiple successive down-sampling operations in the current change detection algorithms using fully convolutional networks (FCNs), a multi-resolution parallel structure is introduced in HDANet, and the image information with different resolutions is comprehensively employed, without any spatial information loss. Moreover, an innovative difference attention module is elaborated for the enhancement of the sensitivity to difference information, to keep the building change information. The experimental results obtained on building change detection datasets confirm that HDANet can improve the differential feature representation for change detection, and the performance of the building change detection is also superior to that of the other advanced change detection methods.

与传统的机器学习方法相比，基于深度学习的变化检测带来了准确性和效率的显着提高。然而，人工物体的差分信息缺乏和尺度特征的多样性等问题是阻碍建筑物变化检测算法应用的关键障碍。为了解决这些问题，本文提出了一种新的基于深度学习的方法，称为高分辨率特征差异注意力网络 (HDANet)。由于连体网络结构，HDANet 可以很好地处理变化特征。为了解决当前使用全卷积网络（FCN）的变化检测算法中多次连续下采样操作导致的建筑物空间特征的丢失，HDANet中引入了多分辨率并行结构，综合利用了不同分辨率的图像信息，没有任何空间信息损失。此外，为了增强对差异信息的敏感性，开发了一个创新的差异注意模块，以保持建筑物的变化信息。在建筑物变化检测数据集上获得的实验结果证实，HDANet可以提高变化检测的差分特征表示，并且建筑物变化检测的性能也优于其他先进的变化检测方法。创新的差异注意模块，用于增强对差异信息的敏感性，以保持建筑物变化信息。在建筑物变化检测数据集上获得的实验结果证实，HDANet可以提高变化检测的差分特征表示，并且建筑物变化检测的性能也优于其他先进的变化检测方法。创新的差异注意模块，用于增强对差异信息的敏感性，以保持建筑物变化信息。在建筑物变化检测数据集上获得的实验结果证实，HDANet可以提高变化检测的差分特征表示，并且建筑物变化检测的性能也优于其他先进的变化检测方法。