Progressive Context-Aware Aggregation Network Combining Multi-Scale and Multi-Level Dense Reconstruction for Building Change Detection.

Building change detection (BCD) using high-resolution remote sensing images aims to identify change areas during different time periods, which is a significant research focus in urbanization. Deep learning methods are capable of yielding impressive BCD results by correctly extracting change features. However, due to the heterogeneous appearance and large individual differences of buildings, mainstream methods cannot further extract and reconstruct hierarchical and rich feature information. To overcome this problem, we propose a progressive context-aware aggregation network combining multi-scale and multi-level dense reconstruction to identify detailed texture-rich building change information. We design the progressive context-aware aggregation module with a Siamese structure to capture both local and global features. Specifically, we first use deep convolution to obtain superficial local change information of buildings, and then utilize self-attention to further extract global features with high-level semantics based on the local features progressively, which ensures capability of the context awareness of our feature representations. Furthermore, our multi-scale and multi-level dense reconstruction module groups extracted feature information according to pre- and post-temporal sequences. By using multi-level dense reconstruction, the following groups are able to directly learn feature information from the previous groups, enhancing the network's robustness to pseudo changes. The proposed method outperforms eight state-of-the-art methods on four common BCD datasets, including LEVIR-CD, SYSU-CD, WHU-CD, and S2Looking-CD, both in terms of visual comparison and objective evaluation metrics.