Building Change Detection in Remote Sensing Images Based on Dual Multi-Scale Attention

Abstract

Accurate change detection continues to pose challenges due to the continuous renovation of old urban areas and the emergence of cloud cover in coastal areas. There have been numerous methods proposed to detect land-cover changes from optical images. However, there are still many flaws in many existing deep learning methods. In response to the problems of unpredictable change details and the lack of global semantic information in deep learning-based change detection models, a change detection model based on multi-scale and attention is proposed. Firstly, a multi-scale attention module is proposed to effectively obtain multi-scale semantic information to build an end-to-end dual multi-scale attention building change detection model. Secondly, an efficient double-threshold automatic data equalization rule is proposed to address the imbalance of data categories existing in the building change detection dataset, which effectively alleviates the severely skewed data distribution and facilitates the training and convergence of the model. The validation experiments are conducted on three open-source high-resolution building change detection datasets. The experimental results show that the proposed method in this paper can detect the location and area of the actual building changes more accurately and has better results in the detail detection part. This verifies the effectiveness and accuracy of the proposed method.