  Traditional synthetic aperture radar image change detection methods based on
convolutional neural networks (CNNs) face the challenges of speckle noise and
deformation sensitivity. 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 multiscale 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 demonstrate the efficiency of
the proposed method. Our codes are available at
https://github.com/summitgao/SAR_CD_MS_CapsNet .
