S-PCNN与二维静态小波相结合的遥感图像融合研究

Remote Sensing Image Fusion Algorithm Based on S-PCNN and Two-Dimensional Stationary Wavelet Transform

在色度、饱和度、纯度(HSV)彩色空间,结合简化脉冲耦合神经网络(S-PCNN)与二维离散静态小波(SWT)提出一种有效的遥感图像融合算法。将多色光谱转换到HSV色彩空间,对多色光谱的V分量与全色光谱进行二维静态小波分解,再将分解后的高频系数输入S-PCNN模型进行融合。低频部分进行第二次小波分解并采用不同规则将其融合,对融合的小波系数进行小波逆变换得到融合的V分量,并将多色光谱的H、S与融合后的V分量转换到RGB空间。通过一组常用的遥感图像融合实验,表明本文算法的融合效果优于传统算法,且融合图像细节明显、色彩保留较好,是一种有效的遥感图像融合算法。

In hue, saturation and value （HSV） color space, an effective remote sensing image fusion algorithm is proposed combining with simplified pulse coupled neural network （S-PCNN） and two-dimensional discrete stationary wavelet transform （SWT）. The multispectral is transformed into HSV color space, the multispectral V component and the panchromatic spectrum are decomposed by two-dimensional static wavelet decomposition, and the decomposed high-frequency coefficients is put into S-PCNN model to fuse.The low-frequency coefficients are decomposed second time and fused with different rules, the fused V component is obtained through wavelet inverse transformation for fused wavelet coefficient, the multispectral H, S components and fused V component are transformed into RGB space. Through a group of common remote sensing images experiment, the results show that the fusion effects of proposed algorithm is better than the traditional algorithms, and the fused image contains lots of detail, color. It is an effective remote sensing image fusion algorithm.