Resolution enhancement of hyperspectral data

Hyperspectral imaging systems are assuming a greater importance for a wide variety of commercial and military systems. The reason for this increased interest is the fact that a hyperspectral sensor of a given spatial resolution or pixel size will reveal information on the scene that are not obtainable by single band or multi-spectral sensors. There have been several approaches to using a single higher spatial resolution band to improve the spatial resolution of the hyperspectral data. In this paper, a new technique for improving the spatial resolution of hyperspectral image data is presented. This technique, called Joint Endmember Determination and Unmixing, combines a high-resolution image with a lower spatial resolution hyperspectral image to produce a product that has the spectral properties of the hyperspectral image at a spatial resolution approaching that of the panchromatic image. Instead of using statistical methods to sharpen hyperspectral imagery, a physical model is used where the data present in both the hyperspectral and high-resolution data are assumed to follow a linear mixing model. In this paper, the new mixture model based resolution enhancement approach is compared to the statistical approach using data from the NASA/JPL AVIRIS hyperspectral sensor.