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Using change detection technique precisely analyzes remote sensing images, it has a broad range of applications in resource surveys, surveillance systems, and map updating. In recent years, deep learning-based methods have become a focus area owing to their excellent feature extraction and representation ability. The fusion of multi-scale features is the key to improving change detection performance in fully convolutional network-based structural methods, primarily based on an architecture with skip connections or nested and dense skip connections. However, these methods only fuse features on the same scale and lack sufficient information from multiple scales to generate appropriate results. Traditional feature fusion with redundant and unsupervised information leads to poor model fitting. To solve these problems, we proposed a novel attention-guided full-scale feature aggregation network (AFSNet). The proposed method used a Siamese structure as the backbone network to extract features, which were then aggregated using full-scale skip connections, and an attention mechanism to avoid feature redundancy. Finally, to obtain a highly accurate final change map, a multiple side-outputs fusion strategy was used to fuse the change maps at different scales. To check the reliability of AFSNet, we tested it on two public datasets, the LEVIR-CD and SVCD datasets. The F1-Score/IoU scores improved by 0.57%/0.95% and 1.14%/2.07% in the two datasets, respectively, compared to those obtained using the methods that achieved suboptimal values. The results showed that AFSNet outperforms other mainstream methods while maintaining a good balance between computational costs and model parameters.
Jiang et al. (Sun,) studied this question.