HFA-Net: High frequency attention siamese network for building change detection in VHR remote sensing images. (September 2022) - Zheng, Hanhong; Gong, Maoguo; Liu, Tongfei; Jiang, Fenlong; Zhan, Tao; Lu, Di; Zhang, Mingyang
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. Abstract: Building change detection (BCD) recently can be handled well under the booming of deep-learning based computer vision techniques. However, segmentation and recognition for objects with sharper boundaries still suffer from the poorly acquired high frequency information, which can result in the deteriorated annotation of building boundaries in BCD. To better obtain the high frequency pattern under the deep learning pipeline, we propose a high frequency attention-guided Siamese network (HFA-Net) in which a novel built-in high frequency attention block (HFAB) is applied. HFA-Net is designed to enhance high frequency information of buildings via HFAB which is composed of two main stages, i.e., the spatial-wise attention (SA) and the high frequency enhancement (HF). The SA firstly guides the model to search and focus on buildings, and HF is employed afterwards to highlight the high frequency information of the input feature maps. With high frequency information of buildings enhanced by HFAB, HFA-Net is able to better detect the edges of changed buildings, so as to improve the performance of BCD. Our proposed method is evaluated on three widely-used public datasets, i.e., WHU-CD, LEVIR-CD, and Google dataset. Remarkable experimental results on these datasets indicate that our proposed method can better detect edges of changed buildings and shows a better performance. The source code will be released at: https://github.com/HaiXing-1998/HFANet .