  Change detection based on remote sensing images has been a prominent area of
interest in the field of remote sensing. Deep networks have demonstrated
significant success in detecting changes in bi-temporal remote sensing images
and have found applications in various fields. Given the degradation of natural
environments and the frequent occurrence of natural disasters, accurately and
swiftly identifying damaged buildings in disaster-stricken areas through remote
sensing images holds immense significance. This paper aims to investigate
change detection specifically for natural disasters. Considering that existing
public datasets used in change detection research are registered, which does
not align with the practical scenario where bi-temporal images are not matched,
this paper introduces an unregistered end-to-end change detection synthetic
dataset called xBD-E2ECD. Furthermore, we propose an end-to-end change
detection network named E2ECDNet, which takes an unregistered bi-temporal image
pair as input and simultaneously generates the flow field prediction result and
the change detection prediction result. It is worth noting that our E2ECDNet
also supports change detection for registered image pairs, as registration can
be seen as a special case of non-registration. Additionally, this paper
redefines the criteria for correctly predicting a positive case and introduces
neighborhood-based change detection evaluation metrics. The experimental
results have demonstrated significant improvements.
