Hyperspectral resolution enhancement with an arbitrary point spread function

Michael T. Eismann; Russell C. Hardie

doi:10.1117/12.553237

15 October 2004 Hyperspectral resolution enhancement with an arbitrary point spread function

Proceedings Volume 5546, Imaging Spectrometry X; (2004) https://doi.org/10.1117/12.553237
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

Several remote sensing platforms have been developed that include boresighted hyperspectral and panchromatic imaging sensors. The NASA EO-1 platform is a prime example, and includes the hyperspectral Hyperion sensor and multispectral Advanced Land Imager (ALI). Typically in these cases, the panchromatic imagery that is produced is of higher spatial resolution than the hyperspectral imagery. In the NASA EO-1 case, for example, Hyperion exhibits a 30 meter ground sample distance (GSD) and the ALI includes a 10 meter GSD panchromatic band. This paper addresses the issue of combining concurrent imagery from both sources with the goal of deriving a hyperspectral image with the spectral resolution of the hyperspectral data source and the spatial resolution of the panchromatic data source. Specifically, the use of a stochastic mixing model (SMM) along with MAP estimation is extended to the case where the point spread function of the hyperspectral sensor is not assumed to be detector-limited. This case is addressed by using an iterative optimization strategy based on a parametric description of the point spread function of the hyperspectral sensor. Results indicate that the iterative approach appears to find the optimal MAP solution. This paper summarizes the MAP/SMM enhancement method, the iterative optimization strategy, and enhancement results.

Citation Download Citation

Michael T. Eismann and Russell C. Hardie "Hyperspectral resolution enhancement with an arbitrary point spread function", Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); https://doi.org/10.1117/12.553237

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