In order to comprehensive utilize infrared and visible imagery weather information, a kind of infrared and visible light satellite cloud image fusion method is put forward based on Nonsubsampled Shearlet Transform (NSST) and adaptive Pulse Coupled Neural Network (PCNN). Firstly, the infrared and visible satellite imagery were decomposed at multi-scale and multi-direction by NSST, then for the low frequency subband coefficients, an self-adaptive fusion rule algorithm based on local area energy and local area variance was presented. The high frequency subband coefficients are fused by an improved adaptive PCNN, The connection strength of pulse coupled neural network is determined by a S type fuzzy membership function according to the different importance of the regional features of high frequency coefficients. Finally, the fusion of low frequency and high frequency were reconstructed by NSST inverse transform. Experimental results show that the proposed method of image fusion is better than the typical fusion method of comparison in this paper both from subjective visual effect and objective evaluation index, and fusion cloud image can provides more rich meteorological data of weather information for the subsequent weather analysis and processing.