A novel interferometric synthetic aperture radar(In SAR) signal processing method based on compressed sensing(CS) theory is investigated in this paper. In SAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. CS theory allows sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. In SAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued In SAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M-sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due to the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. Results on both the simulated data and real data are presented. In comparison with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition. A novel interferometric synthetic aperture radar (In SAR) signal processing method based on compressed sensing (CS) theory is investigated in this paper. In SAR image formation provides the scene reflectivity estimation along azimuth and range coordinates with the height information. While surveying the height information of the illuminated scene, the data volume enlarges. the system can enable the sparse sampling during the data acquisition, which can reduce the data volume and release the pressure on the record devices. In SAR system which configures two antennas to cancel the common backscatter random phase in each resolution element implies the sparse nature of the complex-valued In SAR image. The complex-valued image after conjugate multiplication that only a phase term proportional to the differential path delay is left becomes sparse in the transform domain. Sparse sampling such as M -sequence can be implemented during the data acquisition. CS theory can be introduced to the processing due To the sparsity and a link between raw data and interferometric complex-valued image can be built. By solving the CS inverse problem, the magnitude image and interferometric phase are generated at the same time. In contrast with the conventional SAR interferometry processing results, CS-based method shows the ability to keep the imaging quality with less data acquisition.