Biodiversity assessment using hierarchical agglomerative clustering and spectral unmixing over hyperspectral images.
Sensors (Basel)
; 13(10): 13949-59, 2013 Oct 15.
Article
em En
| MEDLINE
| ID: mdl-24132230
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Plantas
/
Análise Espectral
/
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Ecossistema
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Biodiversidade
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article