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

Gong Chen, Yanan Zhao, Koen Mouthaan

Abstract: 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.