Tomographic synthetic aperture radar (TomoSAR)imaging exploits the antenna aray measurements taken at dif-ferent elevation aperture to recover the reflectivity function along the elevation direction.In these years,for the sparse elevation distribution,compressive sensing (CS) is a developed favorable technique for the high-resolution elevation reconstruction in To-moSAR by solving an L1 regularization problem.However,be-cause the elevation distribution in the forested area is non-sparse,if we want to use CS in the recovery,some basis,such as wavelet,should be exploited in the sparse representation of the elevation reflectivity function.This paper presents a novel wavelet-based L1/2 regularization CS-TomoSAR imaging meth-od of the forested area.In the proposed method,we first con-struct a wavelet basis,which can sparsely represent the eleva-tion reflectivity function of the forested area,and then recon-struct the elevation distribution by using the L1/2 regularization technique.Compared to the wavelet-based L1 regularization To-moSAR imaging,the proposed method can improve the eleva-tion recovered quality efficiently.