Kernel-Adaptive Change Detection Network in Remote Sensing Imagery

Effective representation of features at multiple scales is crucial for remote sensing change detection (RSCD). The latest advancements in Convolutional Neural Networks (CNNs) consistently demonstrate enhanced multiscale representation capabilities, leading to improved performance in RSCD. However, existing multiscale feature extraction methods often require additional module designs, resulting in higher model parameters and computation costs. In this paper, we propose a CNN building block called Kernel-Adaptive (KA) convolution, which utilizes spatial attention generated from different scales to seamlessly integrate effective receptive fields of various sizes within a single network layer. By stacking multiple KA blocks, we construct a lightweight deep network named Kernel-Adaptive Change Detection Network (KANet). Our experiments on widely used datasets, such as LEVIR-CD and CDD, demonstrate that KANet outperforms existing state-of-the-art (SoTA) methods with fewer parameters and FLOPs. Further ablation studies validate the superior multiscale perception capability of KANet compared to existing RSCD methods.