SiUNet3+-CD: a full-scale connected Siamese network for change detection of VHR images

Change detection is a core issue in the study of global change. Inspired by recent success of the UNet3+ architecture originally designed for image semantic segmentation, in this article we proposed a densely connected siamese network for change detection, namely Pre-SiUNet3+-CD (the combination of Pre-processing, Siamese network and UNet3+). First, our proposed pre-processing algorithm can mitigate effects of poor co-registration between bitemporal images, thus alleviating the loss of localization information in the change map. Second, several modifications have been made to UNet3+ in order to improve its fit for change detection tasks using high-resolution imagery and generate highly discriminative and informative representations to locate changed pixels. The effectiveness of the proposed method is demonstrated on several datasets, and experimental results indicated that our model provides very competitive accuracies in terms of precision, recall, F1-score, and visual performance among all the compared methods. This is because it inherits most of the advantages of UNet3+ in object location and boundary production, and the introduction of pre-processing also gives it a significant accuracy boost.