PA-Former: Learning Prior-Aware Transformer for Remote Sensing Building Change Detection

Building change detection (BCD) is significant for urban planning and environmental protection. In view of the interclass similarity and intraclass difference of building changes in complex built-up area, specialized solutions have been introduced in BCD. Mainstream methods include extracting building prior information in advance and enhancing long-range context information. These methods often require additional processing and ignore the construction of cross-temporal context information, resulting in deficiencies on CD performance and efficiency. Therefore, an end-to-end PA-Former for BCD is proposed in this letter, which combines prior extraction and contextual fusion together by learning prior-aware Transformer. Specifically, the PA-Former adopts a prior-feature extractor (PFE) to capture prior and deep features from the bitemporal images, in which a prior interpreter (PI) is integrated to obtain priori structural information of buildings. Besides, a prior-aware Transformer module (PATM) is designed to obtain contextual tokens with spatiotemporal information from the prior features and integrate into the deep features. Extensive experiments with state-of-the-art methods are conducted for comparison. In particular, the PA-Former surpasses the baselines with an F1 of 88.79% on the BCDD dataset and that of 85.32% on the Google dataset.