基于NSST与自适应PCNN相结合的卫星云图融合

Satellite Cloud Image Fusion Based on Adaptive PCNN and NSST

为了综合利用红外和可见光云图的天气信息,本文提出一种基于非下采样shearlet(NSST)与自适应脉冲耦合神经网络(PCNN)相结合的红外和可见光卫星云图融合方法。首先利用NSST对红外和可见光卫星云图进行多尺度、多方向分解,然后对分解得到的低频子带系数采用基于局部区域方差和局部区域能量的自适应加权方法进行融合,高频子带系数采用改进的自适应PCNN进行融合,其中脉冲耦合神经网络的连接强度依据高频系数区域特征的不同重要性,通过一个S型模糊隶属度函数自适应确定。最后对融合完成的低频和高频分量进行NSST逆变换得到最终的融合云图。实验结果表明,基于本文提出方法的融合图像无论是从主观视觉效果,还是客观评价指标都要优于文中对比的典型融合方法,能为后续的天气分析和处理提供具有更加丰富的气象资料。

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.