In microwave remote sensing applications, some areas need to be observed frequently. Compared with the historical observations, the current observation has a little new content. This means that the increment is sparse, making it possible for imaging with under sampled echoes. For the increment imaging, we first establish the linear observation model and the increment model, making it a two-variable optimization problem. Then, the block coordinate descent strategy is used to implement the increment imaging. To estimate the unknown phase matrix, we make the uniform quantification assumption, and an extended Lagrangian multiplier method is applied. For the sparse increment, Bayesian compressed sensing (BCS) is used. Two different resolution scenes are used to verify the feasibility of this algorithm. The performance under different signal-to-noise ratios (SNRs) and undersampling ratios has been discussed as well.