@inproceedings{f0117076696c4edab202aa2f18f4aa94,
title = "Detecting invasive plant species using hyperspectral satellite imagery",
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.",
keywords = "Feature reduction, Hyperion, Hyperspectral, Invasive plants, PCA",
author = "Fuan Tsai and Lin, {En Kai} and Wang, {Hsiang Hua}",
year = "2005",
doi = "10.1109/IGARSS.2005.1525701",
language = "???core.languages.en_GB???",
isbn = "0780390504",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
pages = "3002--3005",
booktitle = "25th Anniversary IGARSS 2005",
note = "2005 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2005 ; Conference date: 25-07-2005 Through 29-07-2005",
}