  Traditional change detection methods based on convolutional neural networks
(CNNs) face the challenges of speckle noise and deformation sensitivity for
synthetic aperture radar images. To mitigate these issues, we proposed a
Multiscale Capsule Network (Ms-CapsNet) to extract the discriminative
information between the changed and unchanged pixels. On the one hand, the
capsule module is employed to exploit the spatial relationship of features.
Therefore, equivariant properties can be achieved by aggregating the features
from different positions. On the other hand, an adaptive fusion convolution
(AFC) module is designed for the proposed Ms-CapsNet. Higher semantic features
can be captured for the primary capsules. Feature extracted by the AFC module
significantly improves the robustness to speckle noise. The effectiveness of
the proposed Ms-CapsNet is verified on three real SAR datasets. The comparison
experiments with four state-of-the-art methods demonstrated the efficiency of
the proposed method. Our codes are available at
https://github.com/summitgao/SAR_CD_MS_CapsNet.
