BIODIVERSITY ASSESSMENT USING HIERARCHICAL CLUSTERING OVER HYPERSPECTRAL IMAGES

被引：0

作者：

Medina, Ollantay ^{[1
]}

Manian, Vidya ^{[2
]}

Chinea, J. Danilo ^{[3
]}

机构：

[1] Univ Puerto Rico, Call Box 9000, Mayaguez, PR 00681 USA

[2] Univ Puerto Rico Mayaguez, Dept Elect & Comp Engn, Mayaguez, PR 00681 USA

[3] Univ Puerto Rico Mayaguez, Dept Biol, Mayaguez, PR 00681 USA

来源：

2012 4TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING (WHISPERS) | 2012年

关键词：

Hyperspectral Images; Biodiversity; Minimum Spanning Tree; Clustering;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Hyperspectral images represent an important source of infonnation to assess ecosystem biodiversity. In particular, plant species richness is a primary indicator of biodiversity. This paper aims to use spectral variance to predict vegetation richness, known as Spectral Variation Hypothesis. A hierarchical clustering method based on minimum spanning tree computations retrieve clusters whose Shannon entropy reflects the species richness on a given zone. These entropies correlate well with the ones calculated directly from field data.

引用

页数：4

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