HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images

•We propose a new convolutional neural network-based Siamese architecture, high frequency attention Siamese network, for a finer recognition of changed building objects in very-high-resolution remote sensed images.•With supplementary high frequency information provided by the high frequency enhancement block, our proposed method can acquire a better ability of feature representation, thus brings expectable performance improvement.•The enhancement of global high frequency information in deep neural networks has been preliminarily confirmed beneficial in building change detection.•Comprehensive comparisons among the recent change detection methods and our proposed method are given, which indicates that our method can achieve state-of-the-art performance in building change detection.