Abstract: In order to improve the fusion quality of panchromatic(PAN) and multispectral(MS) images,and to solve the pro-blems of difficulty in parameter adjustment ofpulse coupled neural network(PCNN)and incomplete preservation of edge features of fused images,this paper proposes a remote sensing image fusion method by combining Canny operator and parameter-adaptive.Firstly,the MS image is converted into HSV color space to obtain thevalue(V) component,and the edge information of PAN image is distinguished to the non-edge by Canny operator.The edge of PAN image is enhanced by fusing the PAN image and V-component of MS image according to the characteristics of edge distribution.Then,the new PAN image and the V-component of MS image are respectively decomposed into their corresponding high-frequency and low-frequency coefficient bands by the nonsubsampled shearlet transform(NSST).The high-frequency bands are fused by a parametric-adaptive PCNN model,in which all the PCNN parameters can be estimated adaptively by the input frequency bands to obtain a PCNN model with optimal parameters.The low-frequency bands are fused by the method of selective weighted summation.Finally,the new V-component is obtained by inverse transform of NSST,and then the final fused image is achieved by inverse transform of HSV.The proposed method is compared with other recent methods,and seven objective evaluation indicators are selected to evaluate the spatial details and spectral information of the fusion image.Experimental results show that the proposed method can obtain better fusion performance with more advantages in visual quality and objective index evaluation.

Key words: Image fusion, Pulse coupled neural network, Canny operator, Shearlet transform, Parameter optimization