A Light-Weight Neural Network Using Multiscale Hybrid Attention for Building Change Detection

The study of high-precision building change detection is essential for the sustainable development of land resources. However, remote sensing imaging illumination variation and alignment errors have a large impact on the accuracy of building change detection. A novel lightweight Siamese neural network building change detection model is proposed for the error detection problem caused by non-real changes in high-resolution remote sensing images. The lightweight feature extraction module in the model acquires local contextual information at different scales, allowing it to fully learn local and global features. The hybrid attention module consisting of the channel and spatial attention can make full use of the rich spatiotemporal semantic information around the building to achieve accurate extraction of changing buildings. For the problems of large span of changing building scales, which easily lead to rough extraction of building edge details and missed detection of small-scale buildings, the multi-scale concept is introduced to divide the extracted feature maps into multiple sub-regions and introduce the hybrid attention module separately, and finally, the output features of different scales are weighted and fused to enhance the edge detail extraction capability. The model was experimented on the WHU-CD and LEVIR-CD public data sets and achieved F1 scores of 87.8% and 88.1%, respectively, which have higher change detection accuracy than the six comparison models, and only cost 9.15 G MACs and 3.20 M parameters. The results show that our model can achieve higher accuracy while significantly reducing the number of model parameters.