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Abstract
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Spectral mixture analysis (SMA) is an effective tool in
recognition of unique spectral signatures of materials called endmembers
and estimating their percentage of existence (abundance
fractions). Most approaches designed in endmember extraction
process are established by applying the spectral information of
the dataset and, thus, tend to neglect the existing spatial correlation
between adjacent pixels. Although several preprocessing
modules have been developed by incorporating both spatial and
spectral properties prior to spectral-based endmember extraction
algorithms (EEs), they still encounter several challenges.
Hence, in this paper, we propose an appropriate clustering and
oversegmentation-based preprocessing (COPP) by greatly benefiting
from the integration of spatial and spectral information.
Moreover, a novel top-down oversegmentation (TDOS) algorithm
is developed which can recognize small oversegments with high
spatial correlation. Our scheme removes oversegments located at
spatial border of cluster regions. Average spectral vectors of determined
spatially homogenous oversegments are considered so that
their spectral purity scores are calculated. COPP identifies spatially
homogenous zones with the greatest spectral purity scores.
Pixels of these regions are more likely to be adopted as endmembers
by means of subsequent EEs. COPP can take advantage of
degrading local spectral variability and noise power. The main
contribution of this paper is the enhanced computational performance
of EE as well as the precise reconstruction of the original
hyperspectral scene besides its appropriate recognition of endmembers’
spectral signatures. The effectiveness of our design and
its validation are appraised with the state-of-the-art strategies on
a synthetic and AVIRIS real hyperspectral datasets.
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