为了提高国产合成孔径雷达(SAR)和光学图像的融合性能,采用高分一号、高分二号多光谱图像,高分三号SAR 聚束模式和精细化条带1模式图像,利用变换域和空间域两种不同的融合思想,提出一种非下采样剪切波变换(NSST)方法结合IHS(intensity-hue-saturation)变换和区域性改进脉冲耦合神经网络(PCNN)方法的融合算法(IHS_NSST)。该算法首先对多光谱图像进行IHS变换;其次在NSST分解的子带上,引用区域性的思想,对低频成分采用区域能量平均方法,对高频成分采用改进拉普拉斯能量和(SML)激励的PCNN方法;最终从定性和定量两个方面进行评价,并将所提算法与多种融合方法作比较。结果表明:基于区域性IHS_NSST的融合方法在高分SAR和光学图像融合上有较大的优势;采用该方法大大提升了融合性能,有效减小了光谱失真,较好地保持空间特征信息,提高了国产高分SAR和光学图像的可利用程度。

To improve the fusion performance of the domestic synthetic aperture radar (SAR) and optical images, multi-spectral images of GF-1 and GF-2 and the SAR spotlight (SL) and fine stripe 1 (FS1) model images of GF-3 are used. Using two different fusion ideas of transform domain and space domain, we propose a fusion algorithm, namely IHS_NSST, based on non-subsampled shearlet wave transform (NSST) combined with the intensity-hue-saturation (IHS) transform and the regional improved pulse coupled neural network (PCNN) method. In this algorithm, IHS transform is firstly performed on multi-spectral images. Secondly, in the sub-band of NSST decomposition, the regional idea is used. The region energy averaging algorithm is used for low frequency components, and the PCNN algorithm based on the improved sum of modified Laplacian (SML) excitation is used for high frequency components. Finally, the proposed algorithm is compared with many fusion methods in terms of qualitative and quantitative evaluations. The results show that the fusion method based on regional IHS_NSST has a great advantage in the fusion of the high resolution SAR and optical images. The proposed method greatly improves the fusion performance, reduces spectral distortion, maintains spatial feature information better, and improves the availability of domestic high resolution SAR and optical images.