Building Change Detection for VHR Remote Sensing Images via Local-Global Pyramid Network and Cross-Task Transfer Learning Strategy

Building change detection (BCD) for very high-spatial resolution (VHR) remote sensing images is very important and challenging in the field of remote sensing, as the building is one of the most significant and valuable man-made ground targets. This paper proposes a local-global pyramid network (LGPNet), which combines a local feature pyramid module (LFPM) and a global spatial pyramid module (GSPM) for various building feature extraction. The LFPM is constructed using the convolutional kernel with three different pyramid scales, and then the local pyramid features are obtained by adding features of each scale. In the GSPM, the global spatial pyramid features are extracted by adaptive average pooling to acquire global contextual information from different fields of view on deep features. The LFPM and the GSPM work in a parallel and complementary manner to capture discriminative features of various buildings. In addition to the LFPM and the GSPM, the proposed LGPNet also employs two general attention mechanisms, i.e., the position attention module and the channel attention module, which can select and emphasize adaptively some building features with high semantic responses. Besides, in order to mitigate the influence of other ground targets to a certain extent, a cross-task transfer learning strategy is introduced to make the LGPNet focus on the building, which significantly improves the performance of our method. Extensive experiments on two public available BCD datasets show that the proposed LGPNet can achieve significant improvement compared with eight other state-of-the-art methods. The source code and the pretrain model will be released at https://github.com/TongfeiLiu/LGPNet.