Sampling, radiometry, and image reconstruction for polar and geostationary meteorological remote sensing systems

In this paper, a Bayesian-based image reconstruction scheme is utilized for estimating a high resolution temperature map of the top of the earth's atmosphere using the GOES-8 (Geostationary Operational Environmental Satellite) imager infrared channels. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the sub-pixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multi-channel imaging system. The temperature estimation scheme is compared to deconvolution via pseudo-inverse filtering using two metrics. One metric is the mean squared temperature error. This metric describes the radiometric accuracy of the image estimate. The second metric is the recovered Modulation Transfer Function (MTF) of the image estimate. This method has traditionally been used to evaluate the quality of image recovery techniques. It will be shown in this paper that there is an inconsistency between these two metrics in that an image with high spatial frequency content can be reconstructed with poor radiometric, accuracy. The ramifications of this are discussed in order to evaluate the two metrics for use in quantifying the performance of image reconstruction algorithms.