Research on the comparison of FCN and U-Net in remote sensing image change detection

Remote sensing image change detection has a wide range of applications in urban planning, disaster monitoring, environmental protection and other fields. Since fully convolutional neural network has a good performance in image processing, it is widely used in remote sensing image change detection, among which U-Net and FCN are two important fully convolutional neural networks. After a comparative analysis of the two neural network structures, it is proposed that the FCN structure has a better ability to extract changed informations. At the same time, a skip connection method CSC is proposed which can enhance the feature extraction ability of FCN. The computational complexity of FCN is almost unchanged after CSC is applied. The change detection capability of CSC-FCN exceeds that of U-Net when the computational complexity is much lower than that of U-Net. It is concluded that the FCN structure has better change detection ability in dealing with multi-channel data containing complex timing information.