부산대학교 도서관

Deep learning (DL)-based architectures have shown a strong capacity to identify changes. However, existing change detection (CD) networks still suffer from limited applicability when it comes to multi-scale targets and spatially misaligned objects. For the sake of tackling the above problems, a region-based feature fusion network (RFNet) for CD of very high spatial resolution (VHR) remote sensing images is proposed. RFNet uses a fully convolutional Siamese network backbone where a multi-stage feature interaction module (MFIM) is embedded in the dual encoder and a series of region-based feature fusion modules (RFFMs) is used to generate change information. The MFIM fuses features in different stages to enhance the interaction of multi-scale information and help the network better distinguish complex ground objects. The RFFM is built based on region similarity (RSIM), which measures the similarity of bitemporal features with neighborhoods. The RFFM can reduce the impact of spatially offset bitemporal targets and accurately identify changes in bitemporal images. We also design a deep supervise strategy by directly introducing RSIM into loss calculation and shortening the error propagation distance. We validate RFNet with two popular CD datasets: the SECOND dataset and the WHU dataset. The qualitative and quantitative comparison results demonstrate the high capacity and strong robustness of RFNet. We also conduct robustness experiments and the results demonstrate that RFNet can deal with spatially shifted bitemporal images. [ABSTRACT FROM AUTHOR]