SSDFL: Spatial scattering decomposition feature learning for PolSAR image - Chen Yang; Biao Hou; Bo Ren; Xu Liu; Jocelyn Chanussot; Shuang Wang; Licheng Jiao (International Journal of Applied Earth Observations and Geoinformation, 2024)
In PolSAR image, the presence of speckle brings significant fluctuations in the information of adjacent pixels. However, most target decomposition (TD) methods only focus on a single pixel then ignore spatial scattering information of neighborhood pixels, resulting in poor stability of decomposition features and low representation ability to terrain targets. In this paper, spatial scattering decomposition feature learning (SSDFL) model is proposed to introduce spatial information into TD. The model contains decomposition feature extraction and polarimetric data reconstruction modules. Specifically, the spatial-level and pixel-level decoders are designed to implement spatial TD and maintain the terrain details, respectively. Since the SSDFL is based on deep model learning, it can learn appropriate decomposition bases and features according to different input data. Furthermore, scattering power and component sparsity restrictions are equipped to enhance the physical scattering meanings of SSDFL model. By experiments on real PolSAR datasets, the results shown spatial information plays an irreplaceable role in TD and it also enhances the resistance to speckle. Meanwhile, the learned decomposition features could reveal the scattering characteristics of terrain targets to some extent. Finally, the effectiveness of decomposition features is verified through the features’ discriminability and the performance on real terrain classification task.