Detecting invasive plant species using hyperspectral satellite imagery

Fuan Tsai, En Kai Lin, Hsiang Hua Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Hyperspectral remote sensing images provide more complete and detailed spectral information about ground coverage and have a great potential to identify specific plant species in vegetation covered areas. The high data dimensionality of hyperspectral data can cause substantial impact to its applications. Principal component analysis is a common technique used for feature reduction in remote sensing image analysis. However, it may also overlook subtle but useful information. This research developed a segmented principal component analysis scheme that can be used to reduce the dimensionality of a hyperspectral image but also retain critical spectral features helpful in discriminating different vegetation types. The developed methodology was applied to the analysis of a Hyperion hyperspectal image to determine the status of an invasive plant species (Leucaena leucocephala) in southern Taiwan.

Original languageEnglish
Title of host publication25th Anniversary IGARSS 2005
Subtitle of host publicationIEEE International Geoscience and Remote Sensing Symposium
Pages3002-3005
Number of pages4
DOIs
StatePublished - 2005
Event2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 - Seoul, Korea, Republic of
Duration: 25 Jul 200529 Jul 2005

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume4

Conference

Conference2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005
Country/TerritoryKorea, Republic of
CitySeoul
Period25/07/0529/07/05

Keywords

  • Feature reduction
  • Hyperion
  • Hyperspectral
  • Invasive plants
  • PCA

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