SAR Change Imaging in the Sparse Transform Domain Based On Block Coordinate Descent Algorithm

Due to the sub-Nyquist sampling, compressive sensing (CS) theory can relieve the contradiction between high-resolution and wide-swath in the field of microwave imaging and it has attracted extensive attention. However, conventional CS-based imaging models always require sparse properties of the unrecovered scene. This paper proposes a synthetic aperture radar (SAR) change imaging in the transforming domain based on CS algorithms, which converts the recovery of the observed scene to that of scene change between the historical observation and the current observation. Firstly, in the sparse transforming domain constructed by historical observation, a new complex-data sparse microwave imaging model is built by the amplitude-phase separated operation. And then a block coordinate descent algorithm is used to recover the change with sub-Nyquist sampling echoes. At last, 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 SAR images.