In order to further restrain the non-uniform noise of remote sensing images, we analyze the the causes and noise models of stripe noise in the spatial remote sensing hyperspectral image, and then propose a moment matching algorithm based on the window threshold decision. The window threshold can be estimated based on the flat region and the obviously striped region. Further, moment matching can be achieved with respect to the images containing stripe noise based on the referent mean, standard deviation, and stripe threshold determination. The experimental results denote that compared with the traditional methods, the peak signal-to-noise ratio increases by at least 6.2163 dB, the mean-square error decreases by at least 5.9630, and the structural similarity increases by at least 0.254. When compared with the traditional methods, an improved image variation inverse coefficient can be obtained using the proposed method; further, the lateral gradient and standard deviation of the image decrease, the image stripe noise is effectively removed, and the original image details are preserved.