SAR Change Imaging in the Sparse Transforming Domain Based on Compressed Sensing

Since compressed sensing (CS) theory broke through the limitation of the traditional Nyquist sampling theory, it has attracted extensive attention in the field of microwave imaging. However, conventional CS-based imaging models always suffer from the limitation of sparse properties of the scene itself. In this article, a novel change imaging in the transforming domain based on CS is proposed, which converts the recovery of the scene itself to that of scene change from the historical observation to the current observation. First, a new complex-data sparse microwave imaging model in the transforming domain is built by the real-imaginary separated operation. Then, a scene transform method named inverse-whitening processing is introduced to confirm the relationship between the real part, imaginary part, and amplitude part of a complex scene, and the sparse transforming domain is constructed based on this processing and historical observation. At last, a CS algorithm is used to recover this change with undersampling echo, and the scene of the current observation can be achieved by integrating the recovered change with the historical observation. The effectiveness of change imaging in the transforming domain is verified on both simulated and real synthetic aperture radar (SAR) images.