Visualization of hyperspectral images

Hyperspectral images provide an innovative means for visualizing information about a scene or object that exists outside of the visible spectrum. Among other capabilities, hyperspectral image data enable detection of contamination in soil, identification of the minerals in an unfamiliar material, and discrimination between real and artificial leaves in a potted plant that are otherwise indistinguishable to the human eye. One of the drawbacks of working with hyperspectral data is that the massive amounts of information they provide requiring efficient means of being processed. In this study wavelet analysis was used to approach this problem by investigating the capabilities it provides for producing a visually appealing image from data that have been reduced in the spatial and spectral dimensions. We suggest that a procedure for visualizing hyperspectral image data that uses the peaks of the spectral signatures of pixels of interest provides a promising method for visualization. Using wavelet coefficients and data from the hyperspectral bands produces noticeably different results, which suggests that wavelet analysis could provide a superior means for visualization in some instances when the use of bands does not provide acceptable results.