Abstract
In recent years, deep learning has become the mainstream development
direction in the change detection field, and its accuracy and speed have
also reached a high level. However, the change detection method based on
deep learning cannot predict all the change areas accurately, and its
application is limited due to local prediction defects. For this reason,
we propose an interactive change detection network (ICD) for very high
resolution (VHR) based on a deep convolution neural network. The network
integrates positive and negative click information in the distance layer
of the change detection network, and users can correct the prediction
defects by adding clicks. We carried out experiments on the open source
dataset WHU and LEVIR-CD. By adding clicks, their F1-scores can reach
0.920 and 0.912, respectively, which are 4.3% and 4.2% higher than the
original network. To better evaluate the correction ability of clicks,
we propose a set of evaluation index—click correction ranges, which is
suitable for evaluating clicks, and we carry out experiments on the
above models. The results show that the method of adding clicks can
effectively correct the prediction defects and improve the result
accuracy.