Mangrove ecosystem study is one of the main beneficiaries of the application of hyperspectral data and spectral
matching techniques. Diversity and density of mangrove species leads to complexity of the ecosystem. Hence, species
level mapping becomes difficult. Though hyperspectral images are appropriate for such a mapping, different mangrove
species with closely matching spectra pose a challenge. This paper proposes a novel hyperspectral matching algorithm
by integrating the stochastic Jeffries-Matusita measure (JM) and deterministic Spectral Angle Mapper (SAM) to
accurately map most species of the mangrove ecosystem. The JM-SAM algorithm signifies the combination of an
quantitative angle measure (SAM) and an qualitative distance measure (JM). The spectral capabilities of both the
measures are orthogonally projected using tangent and sine functions to result in the combined algorithm. The developed
JM-SAM algorithm is implemented to discriminate the mangrove species and the landcover classes of Pichavaram and
Muthupet mangrove forests of southern India using the Hyperion datasets. The developed algorithm is extended in a
supervised framework for improved classification of the Hyperion image. The reference spectra of the mangrove species
and other cover types are extracted from the Hyperion image. From the values of relative spectral discriminatory
probability and relative discriminatory entropy value, it can be inferred that hybrid JM-SAM matching measure results in
improved discriminability than the individual SAM and JM algorithms. This performance is reflected in the
classification results where the JM-SAM (TAN) and JM-SAM (SIN) matching algorithms yielded an improved accuracy
of (86.25%,85%) and (88.10%, 86.96) for both the study sites.
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