There is a substantial signal-processing challenge associated with large-scale (especially global-scale) remote-sensing problems: solving the statistical inverse problem (i.e. deducing the properties of the sensed field from measurements) by brute force, that is by covariance matrix inversion, is completely impractical for fields involving millions of pixels. This paper reports on the ongoing development of an alternative technique, in which the statistical problem is modeled on a multiscale tree, applied to estimating sea-surface temperature (SST) based on infrared radiance observations from the along-track scanning radiometers (ATSRs)

}, keywords = {along-track scanning radiometers, covariance matrix inversion, geophysical signal processing, global-scale estimation problems, hierarchical methods, image processing, infrared radiance observations, inverse problems, measurements, multiscale tree, oceanographic techniques, parameter estimation, radiometry, Remote Sensing, remote-sensing, sea-surface temperature, sensed field, signal-processing, statistical analysis, statistical inverse problem, statistical problem, tree data structures}, doi = {http://dx.doi.org/10.1109/CCECE.1998.682707}, author = {P Fieguth and M R. Allen and M J. Murray} }