Abbe's method and Hopkin's method are among the most popular microlithography aerial image simulation methods. In particular, Hopkin's method is generally more popular for the high speed aerial image simulation domain, and it is used in model-based optical proximity correction. This is due to a general perception that Hopkin's method can generate more compact sets of kernels compared with Abbe's method, due to the application of a singular-value decomposition (SVD) process to Hopkin's large transmission cross coefficient matrix. On the other hand, the primitive Abbe's method is very simple, since it only needs to decompose the source field into independent point sources with a two-dimensional partitioning criteria. Albeit its simplicity, compared with Hopkin's method, in general, Abbe's method, generates a larger set of kernels. In this article the authors propose applying SVD to the original Abbe's kernels, the essential kernels according to their singular values. Experimental results show that the algorithm, the Abbe-SVD method, accomplishes over 68 times of both runtime and memory saving over the traditional Hopkin's SVD method for kernel generation.