  Remote sensing image change detection is of great importance in disaster
assessment and urban planning. The mainstream method is to use encoder-decoder
models to detect the change region of two input images. Since the change
content of remote sensing images has the characteristics of wide scale range
and variety, it is necessary to improve the detection accuracy of the network
by increasing the attention mechanism, which commonly includes:
Squeeze-and-Excitation block, Non-local and Convolutional Block Attention
Module, among others. These methods consider the importance of different
location features between channels or within channels, but fail to perceive the
differences between input images. In this paper, we propose a novel image
difference attention network (IDAN). In the image preprocessing stage, we use a
pre-training model to extract the feature differences between two input images
to obtain the feature difference map (FD-map), and Canny for edge detection to
obtain the edge difference map (ED-map). In the image feature extracting stage,
the FD-map and ED-map are input to the feature difference attention module and
edge compensation module, respectively, to optimize the features extracted by
IDAN. Finally, the change detection result is obtained through the feature
difference operation. IDAN comprehensively considers the differences in
regional and edge features of images and thus optimizes the extracted image
features. The experimental results demonstrate that the F1-score of IDAN
improves 1.62% and 1.98% compared to the baseline model on WHU dataset and
LEVIR-CD dataset, respectively.
