一种基于ASR和PAPCNN的NSCT域遥感影像融合方法

针对稀疏字典的高冗余性和脉冲耦合神经网络（PCNN）参数设置的主观性问题，提出一种结合自适应稀疏表示（ASR）和参数自适应脉冲耦合神经网络（PAPCNN）的非下采样轮廓波变换（NSCT）域遥感影像融合方法。该方法将多光谱影像通过YUV空间变换得到的亮度分量Y与全色影像进行NSCT分解为高低频子带。对低频子带采用基于ASR的融合规则，根据影像块的梯度信息实现自适应稀疏表示。对高频子带采用PAPCNN模型，以选择PCNN的最优参数，再经过相应逆变换得到融合结果。实验结果表明：该方法对不同卫星影像在定性和定量评价上的总体效果均优于其他8种方法。

A Remote Sensing Image Fusion Method based on ASR and PAPCNN in NSCT Domain

In order to solve the problems of the high redundancy of the sparse dictionary and the subjectivity of Pulse-Coupled Neural Network （PCNN） parameter setting， a remote sensing image using fusion method based on Adaptive Sparse Representation （ASR） and Parameter Adaptive Pulse Coupled Neural Network （PAPCNN） in Non-Subsampled Contourlet Transform （NSCT） domain is proposed in this paper. Luminance components and panchromatic images are decomposed by NSCT to obtain high and low frequency sub-bands， and the luminance component Y is obtained from the multi-spectral image through YUV spatial transformation. ASR-based fusion rules are used for sparse representation of low frequency sub-band and adaptive sparse representation is realized according to the gradient information of the image block. The PAPCNN model is adopted to select the optimal parameters of PCNN in the high frequency sub-band. Finally， the fusion result is obtained through the corresponding inverse transformation. The experimental results of different satellite images show that the overall effect of the proposed method is better than the other six methods by using qualitative evaluation and quantitative evaluation.