Hyperspectral Texture Synthesis Using Histogram and Power Spectral Density Matching

Image synthesis tools provide a means for generating hyperspectral image data without the expense of data collection. An important use of these tools is to provide data for the assessment of image exploitation algorithms. However, the detailed spectral/spatial structure of synthetic images is typically not sufficiently realistic to support the prediction of algorithm performance on real data. In this paper, we develop a new method for hyperspectral texture synthesis that accurately simulates the spectral/spatial structure of real hyperspectral image data. The method uses the multiband histogram to model spectral properties and a 3-D Fourier representation to model within-band and cross-band spatial properties. Since multiband histogram equalization does not have a unique solution, we employ a sorting-based method for equalizing multiband distributions that is efficient and produces an exact histogram match. Spatial properties are matched by equalizing the power spectral density (psd) derived from the 3-D Fourier representation. An iterative scheme is used to equalize the histogram and psd for an input and synthesized image. Experiments show that the iteration tends to converge after a small number of steps. A subspace correction method is used to further refine the spectral accuracy of the synthesized image. We demonstrate the utility of the new technique by presenting real and synthesized images and by analyzing spectral angle deviation from the mean functions that describe spectral properties.