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.
We describe a novel approach to produce color composite images from hyperspectral data using weighted spectra averages. The weighted average is based on a sequence of numbers (weights) selected using pixel value information and interband distance. Separate sequences of weights are generated for each of the three color bands forming the color composite image. Tuning of the weighting parameters and emphasis on different spectral areas allows for emphasis of one or other feature in the image. The produced image is a distinct approach from a regular color composite result, since all the bands provide information to the final result.
The algorithm was implemented in high level programming language and provided with a user friendly graphical interface. The current design allows for stand-alone usage or for further modifications into a real time visualization module. Experimental results show that the weighted color composition is an extremely fast visualization tool.