Singular Unit Restoration in InSAR Using Complex-Valued Neural Networks in the Spectral Domain

Interferograms obtained by interferometric synthetic aperture radar generally include many singular points (SPs) originating from interference distortion and noise in the measurement. The filtering process is one of the key techniques in the generation of an accurate digital elevation model (DEM). In this paper, we propose a filter to remove SPs and related distortion using a complex-valued neural network in the spectral domain. It removes SPs nonlinearly and adaptively by referring to the statistics in neighboring windows in the 2-D frequency domain, where the textural features are represented in a more continuous manner than in the real space domain. Experiments demonstrate that the proposed method removes SPs and the distortion in SP-constructing four pixels, namely, the singular unit, more effectively than the conventional filters, resulting in the generation of a more accurate DEM.