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.