The non-local mean denoising (NLM) exploits the fact that similar neighborhoods can occur anywhere in the image and can contribute to denoising. However, these current NLM methods do not aim at multichannel remote sensing image. Smoothing every band image separately will seriously damage the spectral information of the multispectral image. Then the authors promote the NLM from two aspects. Firstly, for multispectral image denoising, a weight value should be related to all channels but not only one channel. So for the kth band image, the authors use sum of smoothing kernel in all bands instead of one band. Secondly, for the patch whose spectral feature is similar to the spectral feature of the central patch, its weight should be larger. Bringing the two changes into the traditional non-local mean, a new multispectral non-local mean denoising method is proposed. In the experiments, different satellite images containing both urban and rural parts are used. For better evaluating the performance of the different method, ERGAS and SAM as quality index are used. And some other methods are compared with the proposed method. The proposed method shows better performance not only in ERGAS but also in SAM. Especially the spectral feature is better reserved in proposed NLM denoising.