  Recently, synthetic aperture radar (SAR) image change detection has become an
interesting yet challenging direction due to the presence of speckle noise.
Although both traditional and modern learning-driven methods attempted to
overcome this challenge, deep convolutional neural networks (DCNNs)-based
methods are still hindered by the lack of interpretability and the requirement
of large computation power. To overcome this drawback, wavelet scattering
network (WSN) and Fourier scattering network (FSN) are proposed. Combining
respective merits of WSN and FSN, we propose Stockwell scattering network (SSN)
based on Stockwell transform which is widely applied against noisy signals and
shows advantageous characteristics in speckle reduction. The proposed SSN
provides noise-resilient feature representation and obtains state-of-art
performance in SAR image change detection as well as high computational
efficiency. Experimental results on three real SAR image datasets demonstrate
the effectiveness of the proposed method.
