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Sensors 2013, 13(10), 13949-13959; doi:10.3390/s131013949
Article

Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images

1,* , 2
 and
3
Received: 7 August 2013 / Revised: 29 September 2013 / Accepted: 30 September 2013 / Published: 15 October 2013
(This article belongs to the Section Remote Sensors)
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Abstract

Hyperspectral images represent an important source of information to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper uses spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. Hierarchical agglomerative clustering is our primary tool to retrieve clusters whose Shannon entropy should reflect species richness on a given zone. However, in a high spectral mixing scenario, an additional unmixing step, just before entropy computation, is required; cluster centroids are enough for the unmixing process. Entropies computed using the proposed method correlate well with the ones calculated directly from synthetic and field data.
Keywords: hyperspectral images; biodiversity; hierarchical clustering hyperspectral images; biodiversity; hierarchical clustering
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Medina, O.; Manian, V.; Chinea, J.D. Biodiversity Assessment Using Hierarchical Agglomerative Clustering and Spectral Unmixing over Hyperspectral Images. Sensors 2013, 13, 13949-13959.

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