Texture feature extraction has always been a hot topic and a source of difficulty in remote sensing image analysis. Most existing studies on texture feature extraction methods use single-band grayscale remote sensing images. How the texture feature can be extracted from a multi-band color remote sensing image is a relatively new field in multispectral remote sensing. The paper presents a color remote sensing image fractal dimension estimation method based on manifold learning. With the use of locally linear embedding

the 5D Euclidian hyperspace

which consists of color attributes

has been reduced

and then the reduced color attributes have been used for fractal dimension estimation. Experimental results on the Landsat-7 satellite data and GeoEye-1 satellite data show that the presented method has a smaller fitting error compared with other fractal dimension estimation methods. For instance

the average fitting errors

of four other fractal dimension estimation methods are 26.2

5

26.3

and 5 times more than the average fitting error

of the presented method. In addition

the presented method can provide not only a fractal dimension with better classification feature but also a more robust fractal dimension than the four other methods. For medium-and low-resolution true-color remote sensing images

medium-and low-resolution false-color remote sensing images

and high-resolution pan-sharpened remote sensing images

the presented method can use the color attribute information of different types of terrains to extract the corresponding texture feature information and then improve the ability of fractal dimension to distinguish different types of terrains. This work may prove useful to follow-up studies on the distributions of different types of terrains in several areas and further regional planning and development based on different distribution characteristics. Future research can add other spectra and analyze the effects of different spectral combinations on the effectiveness of the color texture feature.