  We present a new and effective approach for Hyperspectral Image (HSI)
classification and clutter detection, overcoming a few long-standing challenges
presented by HSI data characteristics. Residing in a high-dimensional spectral
attribute space, HSI data samples are known to be strongly correlated in their
spectral signatures, exhibit nonlinear structure due to several physical laws,
and contain uncertainty and noise from multiple sources. In the presented
approach, we generate an adaptive, structurally enriched representation
environment, and employ the locally linear embedding (LLE) in it. There are two
structure layers external to LLE. One is feature space embedding: the HSI data
attributes are embedded into a discriminatory feature space where
spatio-spectral coherence and distinctive structures are distilled and
exploited to mitigate various difficulties encountered in the native
hyperspectral attribute space. The other structure layer encloses the ranges of
algorithmic parameters for LLE and feature embedding, and supports a
multiplexing and integrating scheme for contending with multi-source
uncertainty. Experiments on two commonly used HSI datasets with a small number
of learning samples have rendered remarkably high-accuracy classification
results, as well as distinctive maps of detected clutter regions.
