Remote sensing (RS) image change detection (CD) is a critical technique of detecting land surface changes in earth observation. Deep learning (DL)-based approaches have gained popularity and have made remarkable progress in change detection. The recent advances in DL-based methods mainly focus on enhancing the feature representation ability for performance improvement. However, deeper networks incorporated with attention-based or multiscale context-based modules involve a large number of network parameters and require more inference time. In this paper, we first proposed an effective network called 3M-CDNet that requires about 3.12 M parameters for accuracy improvement. Furthermore, a lightweight variant called 1M-CDNet, which only requires about 1.26 M parameters, was proposed for computation efficiency with the limitation of computing power. 3M-CDNet and 1M-CDNet have the same backbone network architecture but different classifiers. Specifically, the application of deformable convolutions (DConv) in the lightweight backbone made the model gain a good geometric transformation modeling capacity for change detection. The two-level feature fusion strategy was applied to improve the feature representation. In addition, the classifier that has a plain design to facilitate the inference speed applied dropout regularization to improve generalization ability. Online data augmentation (DA) was also applied to alleviate overfitting during model training. Extensive experiments have been conducted on several public datasets for performance evaluation. Ablation studies have proved the effectiveness of the core components. Experiment results demonstrate that the proposed networks achieved performance improvements compared with the state-of-the-art methods. Specifically, 3M-CDNet achieved the best F1-score on two datasets, i.e., LEVIR-CD (0.9161) and Season-Varying (0.9749). Compared with existing methods, 1M-CDNet achieved a higher F1-score, i.e., LEVIR-CD (0.9118) and Season-Varying (0.9680). In addition, the runtime of 1M-CDNet is superior to most, which exhibits a better trade-off between accuracy and efficiency.

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Change detection is an important task in remote-sensing image analysis. With the widespread development of deep learning in change detection, most of the current methods improve detection performance by making the network deeper and wider, but ignore the inference time and computational costs of the network. Therefore, this paper proposes a lightweight change-detection network called Shuffle-CDNet. It accepts the six-channel image that concatenates the bitemporal images by channel as the input, and it adopts the backbone network with channel shuffle operation and depthwise separable convolution layers. The classifier uses a lightweight atrous spatial pyramid pooling (Light-ASPP) module to reduce computational costs. The edge-information feature extracted by a lightweight branch is integrated with the shallow and deep features extracted by the backbone network, and the spatial and channel attention mechanisms are introduced to enhance the expression of features. At the same time, logit knowledge distillation and data augmentation techniques are used in the training phase to improve detection performance. Experimental results showed that the proposed method achieves a better balance in computational efficiency and detection performance compared with other advanced methods.