Two AVIRIS hyperspectral images selected from the Los Angeles area, one rep
resenting urban and the other rural, were used to examine their spatial com
plexity across their entire spectrum of the remote sensing data. Using the
ICAMS (Image Characterization And Modeling System) software, we computed th
e fractal dimension values using the isarithm and triangular prism methods
for all 224 bands in the two AVIRIS scenes. The resultant fractal dimension
s reflect changes in image complexity across the spectral range of the hype
rspectral images. Both the isarithm and triangular prism methods detect unu
sually high D values on the spectral bands that fall within the atmospheric
absorption and scattering zones where signal-to-noise ratios are low. Frac
tal dimensions for the urban area resulted in higher values than for the ru
ral landscape, and the differences between the resulting D values are more
distinct in the visible bands. The triangular prism method is sensitive to
a few random speckles in the images, leading to a lower dimensionality. On
the contrary the isarithm method will ignore the speckles and focus on the
major variation dominating the surface, thus resulting in a higher dimensio
n. It is seen where the fractal curves plotted for the entire bandwidth ran
ge of the hyperspectral images could be used to distinguish landscape types
as well as for screening noisy bands.