SSN: Stockwell Scattering Network for SAR Image Change Detection
This is an incremental improvement for SAR image analysis, enhancing noise resilience and efficiency in change detection.
The paper tackled SAR image change detection by proposing the Stockwell scattering network (SSN) to address speckle noise and computational inefficiencies, achieving state-of-the-art performance with high computational efficiency on three real datasets.
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.