It is a big challenge to segment remote sensing images especially multispectral satellite imagery due to their unique features. In consideration of the fact that satellite imagery are playing an increasingly important role, we conducted the research on segmentation of such imagery. Since multispectral satellite imagery are more similar to natural color images than to other types of images, it is more likely that studies on natural color images segmentation can be extended to multispectral satellite imagery. The obstacle of applying these studies into multispectral satellite imagery lies into their inefficiency when dealing with the large size of images. Therefore, based on a natural color image segmentation approach -- JSEG, we proposed a more efficient one. First, a grid-based cluster initialization approach is proposed to obtain the initial cluster centers, based on which, a fast image quantization approach is implemented. Second, a feature image named J-image to describe local homogeneity is obtained. Then a watershed approach is applied to the J-image, and initial segmentation results are obtained. At last, based on the histogram similarity of each region, a simplified growth merging approach is proposed and the final segmentation results are obtained. By comparing the result of the JSEG approach and the proposed one, we found that the latter is rather efficient and accurate. Advice on further studies is also presented.