In synthetic aperture radar (SAR) image change detection, it is quite
challenging to exploit the changing information from the noisy difference image
subject to the speckle. In this paper, we propose a multi-scale spatial pooling
(MSSP) network to exploit the changed information from the noisy difference
image. Being different from the traditional convolutional network with only
mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels
are equipped in a convolutional network to exploit the spatial context
information on changed regions from the difference image. Furthermore, to
verify the generalization of the proposed method, we apply our proposed method
to the cross-dataset bitemporal SAR image change detection, where the MSSP
network (MSSP-Net) is trained on a dataset and then applied to an unknown
testing dataset. We compare the proposed method with other state-of-arts and
the comparisons are performed on four challenging datasets of bitemporal SAR
images. Experimental results demonstrate that our proposed method obtains
comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms
other state-of-art methods, especially on the Sendai-A and Sendai-B datasets
with more complex scenes. More important, MSSP-Net is more efficient than
S-PCA-Net and convolutional neural networks (CNN) with less executing time in
both training and testing phases
