Enhanced Feature Interaction Network for Remote Sensing Change Detection

In the current research on remote sensing image change detection (CD), the effective learning of mutual interactions between bi-temporal features has often been overlooked. To address this concern, we introduce a patch exchange block (PEB) aimed at capturing the interplay between bi-temporal channels by exchanging feature patches. This approach preserves feature structural information and prevents the introduction of unnecessary noise. Specifically, the feature maps of bi-temporal images are unfolded into multiple patches, followed by mutual patch exchanges and subsequent fusion operations. Additionally, we seek to leverage Transformers to tackle the model's lack of effective global feature extraction capability. However, the standard Transformer aggregates features based on all query-key pairs, making the model susceptible to irrelevant features' interference. Considering this, we introduce a Sparse Transformer in the decoder. It guides the model's attention to areas of interest by selectively weighting the values produced by the $Q$ and K operations, thus reducing interference from irrelevant information and focusing on the most valuable insights. Through experiments conducted on multiple datasets, we substantiate the effectiveness of our proposed EFIN approach.