To alleviate the heavy dependence of deep learning methods on large-scale high-cost pixel-level annotations,in this paper,we propose a novel weakly supervised method,named MDF-LSR-Net,for high-resolution remote sensing building change detection.Specifically,the proposed method first designs a multi-scale difference feature aggregation module to make better use of multi-scale difference features to generate change heatmaps.Then,by utilizing the local spatial consistency of the low-level fused difference features,MDF-LSR-Net presents a local spatial refinement module to enhance the integrity and accu-racy of change regions in heatmaps.Finally,the change detection model is trained based on the high-quality change heatmaps.Experimental results on publicly available datasets,including WHU and LEVIR,demonstrate that our proposed method can obtain more integral and accurate change heatmaps,leading to significantly improved detection performance of the final change detection model.The final model has achieved over 65％points in IOU and over 79％points in F1 on the WHU dataset.
维普
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