The imaging performance of full polarization synthetic aperture radar (SAR) can be improved by applying the sparse reconstruction technology based on compressed sensing (CS) to different polarizationchannel data independently. However, with respectively independent processing the method cannot utilize the redundancy and complementarity of the polarization information, which may destroy the integrity of the polarization information. A new jointsparsity measure function is built according to the scattering characteristics of radar target in full polarization condition. Then, the full polarization SAR highresolution imaging can be mathematically converted to a multichannel joint sparse constraint optimal reconstruction problem, which can be solved via the improved orthogonal matching pursuit algorithm. Because of effectively using the full polarization information, compared with the singlechannel CS imaging, the multichannel joint CS imaging not only performs better with fewer measurements and obtains better imaging quality, but also fully and accurately reflects the fully polarization scattering characteristics of the target. Finally, the processing of the Backhoe excavator simulation data verifies the effectiveness of the proposed method; a full polarization SAR hardwareinloop system was constructed in an anechoic chamber, the full polarization test data obtained on the system further verify the engineering feasibility of the proposed method.