Most of the development work on automated Machine Vision for space operations has assumed the presence of a dark sky background or a 'cooperative', (i.e.: marked or lighted), target. In reality, the sun-lit earth, or other natural body, will be the background much of the time, providing a far more difficult image segmentation problem. Fortunately, many of the natural background objects, e.g.: clouds, mountain ranges, etc., exhibit fractal characteristics when viewed from orbit. Images of manmade objects such as satellites, space shuttles, and stations will yield sufficiently different values for the fractal parameters so that edge detection and segmentation can be accomplished. This paper describes the methods used to segment images of space scenes into manmade and natural components using fractal dimensions and lacunarities. The calculation of these parameters are described in detail, and the results presented for a variety of aerospace images.