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Change detection is a basic remote sensing image processing task in agriculture, urban planning, disaster assessment, and other fields. Fully convolutional siamese network is currently employed as the primary solution for remote sensing change detection. Fully convolutional networks lead to loss of accurate spatial location information during downsampling. This directly affects the edge accuracy of remote sensing change detection. In the prior method, researchers introduce attention mechanisms and dense networks to improve the accuracy of change detection methods. These approaches often result in a proliferation of network parameters and increased computational requirements, undermining their suitability for real-world, time-sensitive applications such as disaster relief efforts. With the objective of addressing computational and parameter inefficiencies of existing change detection models, we propose the lightweight composite convolution network (LWCCNet). This involves the construction of a downsampling network based on a composite convolution operator, in conjunction with a novel spatial attention mechanism-edge attention module. In addition, we construct a lightweight feature pyramid network (LW-FPN) to fuse multi-scale features. We conduct experiments on the CDD (Change Detection Dataset) dataset and the WHU building change detection dataset. On the CDD dataset, LWCCNet reduces the number of parameters by 86.3  $\&#x0025;$   while the accuracy is reduced by only 1.9  $\&#x0025;$  , indicating that LWCCNet achieves better lightweighting while ensuring accuracy.

LWCCNet: Lightweight Remote Sensing Change Detection Network Based on Composite Convolution Operator