Fusion algorithm of hyperspectral images based on wavelet transform and multi-channel PCNN

A novel fusion algorithm based on Pulse Coupled Neural Networks (PCNN) is proposed to fuse multi-band of hyperspectral images. Using wavelet transform to decompose images into multi-levels for extracting the approximation and high frequency information in different orientations. Each sub-band in wavelet domain of multiple images is input to a multi-channel PCNN model to nonlinear fusion. Then the fused approximation sub-band is generated directly using the firing map. Various high frequency sub-bands are segmented into different regions by the histogram vector center of gravity and deviation of the firing matrix and are fused by different fusion rules in different regions. The fusion image is reconstructed by inverse wavelet transform. Experiment results of OMIS images show that the proposed algorithm can effectively fuse multi-band of hyperspectral images and derive rich information of textures and details.