This paper describes a research project evaluating the success of spatially sharpening spaceborne hyperspectral imagery with higher spatial resolution spaceborne multispectral imagery. In November 2001, NASA launched its first operational hyperspectral sensor, Hyperion, on the Earth Observing-1 spacecraft. Designed to study the utility of hyperspectral imagery collected from a space-based platform, Hyperion’s utility is limited for certain applications by its spatial resolution. Successful spatial sharpening of Hyperion imagery with higher spatial resolution spaceborne multispectral imagery such as ASTER or QuickBird should further enhance the value of Hyperion imagery. In a previous two-year research initiative, the primary author demonstrated the utility of sharpening airborne hyperspectral imagery with higher spatial resolution airborne multispectral imagery. In its first year, that study simulated the sharpening process by utilizing a single airborne hyperspectral dataset to derive both a lower spatial resolution hyperspectral cube and a higher spatial resolution multispectral dataset. In its second year, this study utilized two airborne spectral collections on different platforms for the hyperspectral and multispectral datasets, and successfully demonstrated spatial sharpening despite geometry differences between the two platforms. The next logical step is to validate spatial sharpening of space-based hyperspectral imagery using higher resolution spaceborne multispectral imagery. This current research project evaluated the spatial and spectral utility of Hyperion imagery after sharpening with higher spatial resolution spaceborne multispectral data from the ASTER or QuickBird satellites.
Numerous researchers have demonstrated the accuracy and utility of improved spatial resolution multispectral imagery by sharpening it with higher spatial resolution panchromatic imagery. A much more limited number of researchers have sharpened hyperspectral imagery with panchromatic imagery. In this research we have developed an algorithm that spatially sharpens specific ranges of hyperspectral bands with spectrally correlated multispectral bands of a higher spatial resolution to improve the spatial resolution of the hyperspectral imagery while maintaining or improving it's spectral fidelity. Preliminary validation of the algorithm has been conducted using a 7m AVIRIS scene of the Maryland Eastern Shore containing corn, soybean, and wheat fields. This data was used to simulate 28m HSI and 7m MSI that were used in the sharpening process. Initial analysis has verified the spectral accuracy of the sharpened data. In the next phase of the study, airborne spectral data from two different sensors will be used in the sharpening process with the results used as input for USDA/ARS crop yield and stress models.
Band sharpening involving multi-sensor and multi-resolution imagery is an excellent means of utilizing the complementary nature of various data types. The synergistic use of these imagery types can provide additional information that is not independently available in each source. In the case of band sharpening, a higher spatial resolution panchromatic image is fused with a lower spatial resolution multispectral image. This fusion creates a product with the spectral characteristics of the multispectral image and a spatial resolution approaching that of the panchromatic image. The goal of this project was to evaluate MSI band sharpening in four research areas. The first area explored the 'effective ground sample distance' and relative utility of multispectral imagery sharpened with panchromatic imagery. The second area examined interactions between data compression and the band sharpening process. The third area determined the effectiveness of band sharpening using a pair of high resolution sharpening bands covering different regions of the electromagnetic spectrum. The fourth area determined the effect of band sharpening on the accuracy of automated exploitation algorithms such as terrain categorization and normalized difference vegetation index.