SEED: Information-Preserving Feature Exchange for Remote Sensing Change Detection

Remote sensing change detection identifies semantic inconsistency between spatially aligned bi-temporal observations. Most existing methods construct explicit discrepancy features, such as subtraction, addition, concatenation, or learned fusion, before decoding the change map. We revisit this design and propose SEED, a Siamese Encoder--Exchange--Decoder framework that uses parameter-free feature exchange as the only cross-temporal interaction mechanism. With shared encoder and decoder weights, SEED learns change cues directly from exchanged bi-temporal representations. We further formulate feature exchange as a permutation operator. Under pixel consistency and a fixed exchange mask, the transformation is orthogonal, invertible, and preserves paired information, whereas common arithmetic fusion operators that collapse two streams are generally non-invertible. Experiments on five public benchmarks, including SYSU-CD, LEVIR-CD, PX-CLCD, WaterCD, and CDD, and three representative backbones, including Swin Transformer V2, EfficientNet-B4, and ResNet50, show that SEED achieves competitive or superior performance with a compact and interpretable design. Studies on randomized exchange, single-decoder inference, segmentation-to-change-detection conversion, and systematic misregistration further support the flexibility and robustness of the proposed paradigm. Code and full training/evaluation protocols will be released at https://github.com/dyzy41/open-rscd.