Multiscale Context Aggregation Network for Building Change Detection Using High Resolution Remote Sensing Images

作者：Dong, Jie^*; Zhao, Wufan^*; Wang, Shuai

来源：IEEE Geoscience and Remote Sensing Letters, 2022, 19: 8022605.

DOI：10.1109/LGRS.2021.3121094

• Feature extraction
• Spatial resolution
• Data mining
• Training
• Remote sensing
• Buildings
• Optical sensors
• Change detection (CD)
• class imbalance
• deep convolutional network
• optical remote sensing (RS) images

The existing methods of building change detection (CD) using remote sensing (RS) images are still deficient in handling scale variation and class imbalance problems, indicating a decrease in the robustness of small-object detection and pseudo-change information. Thus, a novel building CD framework called the multiscale context aggregation network (MSCANet) is proposed. The high-resolution network is integrated into the feature extracting stage to maintain high-resolution representations throughout the whole process. Then, multiscale context information is aggregated using a scale-aware feature pyramid module (FPM). Recognition performance can be improved from discriminant feature representation learning by using a channel-spatial attention module. Furthermore, a class-balanced loss is proposed to reduce the impact of class imbalance in long-tail datasets. Experimental results from using the LEVIR-CD and SZTAKI AirChange benchmark datasets prove the superiority of the MSCANet over the other baseline methods, with improved maximum F1 scores of 5.28 and 8.47, respectively.