IDAN: Image Difference Attention Network for Change Detection

Understand

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.