This document proposes a method for simultaneously visualizing and segmenting hyperspectral data. The method treats the hyperspectral data set as a single image created by weighted combination of spectral points for each pixel, with the weights optimized to improve human visibility. A piecewise-constant smoothness constraint on the weights prevents blurring in the fused image. Optimizing the weight matrix also provides information to spatially segment the original hyperspectral data set. Experimental results demonstrate the effectiveness of the proposed method.

1. A TECHNIQUE FOR SIMULTANEOUS VISUALIZATION AND SEGMENTATION OF
HYPERSPECTRAL DATA
ABSTRACT
In this paper, we propose an optimization-basedmethod for simultaneous fusion and
unsupervised segmentationof hyper spectral remote sensing images by exploiting redundancyin
the data. The hyperspectral data set is visualized as a singleimage obtained by weighted addition
of all spectral points at eachpixel location in the data set. The weights are optimized to
improvethose statistical characteristics of the fused image, which invoke anenhanced response
from a human observer. A piecewise-constantsmoothness constraint is imposed on the weights
instead of thefused image by minimization of its 3-D total-variation norm, thuspreventing the
fused image from blurring. The optimal recoveryof the weight matrix additionally provides
useful information insegmenting the hyperspectral data set spatially.We provide
ampleexperimental results to substantiate the usefulness of the proposedmethod.