Joint Spatial-Frequency Scattering Network for Unsupervised SAR Image Change Detection

This is the authors' abstract. We don't add key points for this paper.

Synthetic aperture radar (SAR) image change detection is of great importance for many applications but is significantly hampered by the presence of speckle noise. Recent learning-based methods have suppressed speckle noise by exploiting deep feature representations in both spatial and frequency domains. However, these methods are not interpretable while also imposing a high computational burden. Here, an interpretable and efficient spatial-frequency network is presented, which extracts noise-robust features in both spatial and frequency domains simultaneously. Spatially, noise-robust features are generated based on the neighborhood-based ratio (NR) and fed into an extreme learning machine (ELM) as a classifier for its efficiency and effectiveness. In the frequency domain, a shallow broad wavelet scattering network (SWSN) is proposed, which is an adaptation of the deep scattering network (DSN) that offers interpretable and noise-resilient feature representation with reduced depth. Instead of the sequential, layer-by-layer feature extraction approach of the DSN, the SWSN parallelly extracts frequency information, which significantly enhances computational efficiency. The proposed joint spatial-frequency framework provides robustness against speckle noise and obtains state-of-the-art performance as well as high computational efficiency in unsupervised SAR image change detection. Experimental results on two real SAR image datasets demonstrate the effectiveness of the proposed method.