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Abstract
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Most spectral mixture analyses in the literature over-
look the spatial correlation of neighborhood pixels. The main contribution
of this paper is to consider the impacts of both spatial and
spectral information prior to endmember (EM) extraction algorithms.
Hence, we take advantage of a top-down over-segmentation
algorithm in combination with fuzzy c-means (FCM) clustering to
identify spatially homogenous over-segments with minimum spectral
variability and high spatial correlation. FCM provides a soft
segmentation while its partial membership matrix is exploited to
calculate a novel local entropy criterion (LEC) at pixels seated in
homogenous over-segments. Afterwards, by performing an adaptive
threshold per homogenous over-segment, pixels with high LEC
values which have high certainty to associate with only one class
are selected as pure ones LEC calculations lead to preserving level
of unmixing accuracy. While speeding up EM extraction. This subject
is important for large images particularly with real-time limitations.
With respect to experiments accomplished on synthetic
and AVIRIS hyperspectral images, clustering, over-segmentation,
and entropy preprocessing has a simple and fast framework while
it relatively outperforms the state-of-the-art procedures in terms
of extraction accuracy and computing time.
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