Compared with the other two kinds of methods, these methods have obvious advantages due to their ability to perform scattering point separation and the stability of their height estimation, and they are widely used in spaceborne SAR. However, spaceborne SAR requires repeated passes to obtain SAR data for layover separation, which needs to follow pre-defined orbits and usually experiences a temporal baseline of at least several days. The operation mode can lead to temporal incoherence, displacements even of stable scatterers for larger temporal baselines, and a greater waiting time for data delivery, which can be unfavorable for time-critical applications (such as disaster response scenarios) [14]. Compared with spaceborne SAR, airborne sensors have specific advantages, such as flexible timing, almost arbitrary flight planning and a flexible flight path, which make airborne sensors more efficient to run data acquisition tasks. Whereas the number of baselines of airborne multi-baseline SAR systems is usually small (two to four), the Restricted Isometry Property (RIP) and incoherence of the traditional compressive sensing method, which are the prerequisites of sparse reconstruction in the compressive sensing domain, are poor enough because of the low dimension of the measured data, and it is difficult to achieve sparse reconstruction and produce stable and credible results. In this condition, this method is not suitable for layover separation in airborne InSAR system with two to four baselines. Thus, the layover separation methods, especially compressive sensing methods, have poor performance. Moreover, in the traditional compressive sensing approach, the L1 norm-based compressive sensing solution is approximately the optimal solution for the L0 norm under the condition of noncoherent characteristics and constraint, but it has the problems of many outlier values and a long operation time [18,19]. At the same time, this method is mostly used for TomoSAR technology in spaceborne SAR system which requires at least six baselines and is rarely used in airborne SAR systems, as the separation of scatterers and the height estimation accuracy in layover areas will be seriously deviated by the compressive sensing method in the airborne case. Based on the above problems, this paper, based on the idea of multi-look, adopts a multi-look compressive sensing method and a multi-look compressive sensing method based on separable approximate sparse reconstruction. The layover separation method based on multi-look compressive sensing is used to improve the dimensions of measured data in compressive sensing and the stability of noise estimation, which causes the results of the multi-look L1 norm solution method to be closer to those of L0 norm solution. Thus, the sparse reconstruction ability of compressive sensing, the separation ability of the scatterers in layover areas and the stability of height estimation are improved. Likewise, the separable approximate sparse reconstruction method utilizes the threshold iteration method to improve the processing speed. Moreover, this method can effectively reduce the operation time while maintaining the resolution ability of scatterers in layover areas and the height direction estimation accuracy. Simulation and measured data experiments are carried out to verify the effectiveness of the two methods. The rest of this paper is organized as follows. Section 2 first introduces the traditional L1-norm compressive sensing algorithm for layover separation, analyzes its shortcomings, and then introduces the simple multi-look idea to improve the stability of sparse reconstruction. Secondly, aiming at the problem of the long operation time of L1 norm compressive sensing, a separable approximate reconstruction method of compressive sensing for layover separation is proposed. Section 3 introduces the simulation and measured data experiments with the proposed methods. In Section 4, we discuss the results of the proposed methods and show their intrinsic values.