Abstract: Remote sensing change detection plays a crucial role in both military and civilian fields.However,there is a large amount of pseudo-change noise due to the huge spatial and temporal gaps in data acquisition of change detection image pairs.Exis-ting change detection methods are based on learning object features from a dual-stream twin network,followed by pseudo-change noise removal via a series of proprietary networks.However,this mutually independent denoising approach lacks the ability to capture the interdependencies between image pairs,and often results in the loss of a large amount of fine-grained information due to excessive focus on the denoising design.The CFIR proposed in this paper mitigates the problem of fine-grained information loss by exploiting the data structure features of the image pairs to augment the model's ability to learn the contextual dependencies and to compensate for the lost fine-grained information.In addition,it employs a gating mechanism that eliminates pseudo-change noise in the change detection task and guides the network to extract relevant change features,mitigating the impact of extreme data imbalance in change detection on the model's ability to learn real changes.CFIR has demonstrated competitive performance in several change detection benchmarks.Compared with the state-of-the-art method,it improves F1 by 0.21% and IoU by 0.38% on the LEVIR-CD dataset,and improves F1 by 0.99% and IoU by 2.43% on the WHU-CD dataset.

Key words: Remote sensing change detection, Fine-grained information reconstruction, Gated mechanisms, Denoise, Supervised learning