Comparison of physics based processing to orthogonal subspace projection methods for the classification of vegetation in high resolution hyperspectral data

Winter, Edwin M.; Schlangen, Michael J.; Winter, Michael E.

doi:10.1109/acssc.1998.751532

Cited by 1 publication

(4 citation statements)

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“…This was also true when matched filter was applied to a first derivative spectrum. This result is in agreement with previous work [12,13].…”

Section: Discussionsupporting

confidence: 94%

“…Generally, many of the published works will focus on specific types of vegetation analysis such as land cover types, vegetation health, vegetation classification [1,[7][8][9][10][11]. Several discuss on individual species delineation [12][13]. The focus of much of this research is analysis of the chlorophyll red edge position and red well position which has been shown to be the most useful region for estimating many biophysical and biochemical parameters of vegetation [7,[14][15].…”

Section: Vegetation Analysis With Hyperspectral Imagerymentioning

confidence: 99%

“…Since all vegetation is composed of chlorophyll the reflectance spectra from widely differing plant species will look similar [13]. Variation in plant chlorophyll content, moisture, stress, and other parameters will be evident in a shift of the red edge to either longer or shorter wavelengths.…”

Section: Vegetation Analysis With Hyperspectral Imagerymentioning

confidence: 99%

“…Variation in plant chlorophyll content, moisture, stress, and other parameters will be evident in a shift of the red edge to either longer or shorter wavelengths. It is the amount of this shift that can be used to differentiate plant species [13]. This type of analysis has been applied to several hyperspectral sensors to include AVIRIS [1], Hyperion [15], and CASI [16].…”

Section: Vegetation Analysis With Hyperspectral Imagerymentioning

confidence: 99%

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Comparison of spectral matching techniques for vegetation species delineation of the National Arboretum

Salvador

Resmini

2009

SPIE Proceedings

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Identification of differing vegetation species has been a lauded ability of hyperspectral imagery and analysis but continues to be a challenging problem. Hyperspectral imagery has been used for years in applications such as vegetation analysis and delineation, terrain categorization, explosive mine detection, environmental impacts and effects, and agriculture and crop evaluation. Unlike applications which focus on detection of specific targets with constant spectral signatures, vegetation signatures continually vary across their growth cycle. In order to identify various vegetation species, either large collections of time-varying reference signatures are required, or ground truth/training data is needed. These are not always viable options and in many cases only in-scene data can be used. In this study we compare the performance of various spectral matching methods in separating vegetation at the species level. Parametric, nonparametric, derivative techniques, and other methods are compared. These methods are applied to a complex scene, the National Arboretum in Washington DC, which was imaged by an airborne hyperspectral sensor in August, 2008. This survey assesses performance of spectral matching methods for vegetation species delineation and makes recommendations for its application in hyperspectral data analysis.

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“…This was also true when matched filter was applied to a first derivative spectrum. This result is in agreement with previous work [12,13].…”

Section: Discussionsupporting

confidence: 94%