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

Highlights：

• 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.