Publication Date



Technical Report: UTEP-CS-98-16

In: Ali Mohamad-Djafari (ed.), Bayesian Inference for Inverse Problems, Proceedings of the SPIE/International Society for Optical Engineering, Vol. 3459, San Diego, CA, 1998, pp. 138-146.


Satellite imaging is nowadays one of the main sources of geophysical and environmental information. It is, therefore, extremely important to be able to solve the corresponding inverse problem: reconstruct the actual geophysics- or environment-related image from the observed noisy data.

Traditional image reconstruction techniques have been developed for the case when we have a single observed image. This case corresponds to a single satellite photo. Existing satellites (e.g., Landsat) take photos in several (up to 7) wavelengths. To process this multiple-spectral information, we can use known reasonable multi-image modifications of the existing single-image reconstructing techniques. These modifications, basically, handle each image separately, and try to merge the resulting information.

Currently, a new generation of imaging satellites (Lewis) is being launched, that will enable us to collect visual images for about 500 different wavelengths. This two order of magnitude increase in data amount should lead to a similar increase in the processing time, but surprisingly, it does not. An analysis and explanation of this paradoxical simplicity is given in the paper.