Abstract: In order to improve the fusion quality of the multispectral(MS)and panchromatic(PAN)images and achieve a balance between the injection of spatial details and the preservation of spectral information,a pansharpening method based on local adaptive spatial-spectral modulation and cooperation with segmentation is proposed in this paper.k-means algorithm is used to segment MS images into different connected component groups according to their spectral characteristics,then the local adaptive spectral modulation(LASpeM)and local adaptive spatial modulation(LASpaM)coefficients can be acquired based on local component groups.LASpeM coefficient matrix is estimated based on details extracted from MS and PAN images and also spectral relationship between MS bands;LASpaM coefficient matrix is constructed based on local deviation and correlation between the spectral characteristics of MS and low-resolution PAN images.Moreover,cooperation with segmentation is introduced to pansharpening in this paper,the LASpeM and LASpaM coefficients are estimated based on component groups to optimize the fusion image,and the feedback from fusion result is applied to adjust the parameters of the segmentation algorithm.The evaluation of the proposed method is performed in Matlab environment based on data sets from the GeoEye-1 and Quick-Bird satellites.Experimental results show that the proposed method achieves better visual results and objective evaluation indices than seven classic and state-of-the-art fusion methods,and prove that the method is able to balance the spatial and spectral information while reducing spectral distortion.