We present a set of algorithms and a search strategy for the robust
content-based retrieval of multispectral satellite images. Since the
property of interest in these images is usually the physical
characteristics of ground cover, we use representations and methods that
are invariant to illumination and atmospheric conditions. The
representations and algorithms are derived for this application from a
physical model for the formation of multispectral satellite images.
The use of several representations and algorithms is necessary to
interpret the diversity of physical and geometric structure in these
images. Algorithms are used that exploit multispectral distributions,
multispectral spatial structure, and labeled classes. The performance
of the system is demonstrated on a large set of multispectral satellite
images taken over different areas of the United States under different
illumination and atmospheric conditions.