  Majority models of remote sensing image changing detection can only get great
effect in a specific resolution data set. With the purpose of improving change
detection effectiveness of the model in the multi-resolution data set, a
weighted rich-scale inception coder network (WRICNet) is proposed in this
article, which can make a great fusion of shallow multi-scale features, and
deep multi-scale features. The weighted rich-scale inception module of the
proposed can obtain shallow multi-scale features, the weighted rich-scale coder
module can obtain deep multi-scale features. The weighted scale block assigns
appropriate weights to features of different scales, which can strengthen
expressive ability of the edge of the changing area. The performance
experiments on the multi-resolution data set demonstrate that, compared to the
comparative methods, the proposed can further reduce the false alarm outside
the change area, and the missed alarm in the change area, besides, the edge of
the change area is more accurate. The ablation study of the proposed shows that
the training strategy, and improvements of this article can improve the
effectiveness of change detection.
