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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3554

Title: Improving classification accuracy of spectrally similar tree species: a complex case study in the Kruger National Park
Authors: Debba, P
Keywords: Tree classification
Spectral matching
Kruger national park
Classification techniques
Hyperspectral remote sensing
Iterated conditional modes
ICM
Spectral angle mapper classifier
SAM
Species variability
Tree species
K-nearest neighbour classifier
Issue Date: Jul-2009
Citation: Debba, P. 2009. Improving classification accuracy of spectrally similar tree species: a complex case study in the Kruger National Park. Rhodes University, South Africa, July 2009. pp 1-51
Abstract: This presentation to the SASA Eastern Cape Chapter at Rhodes University studies the variability within a species class and the variability between the species classes of seven spectrally similar tree species and presents ways in which the within-species class variability can be reduced compared to the between-species class variability. Furthermore, two classification approaches with spectral angle mapper: (i) using a spectral library composed of one spectrum (endmember) per species and (ii) a multiple endmember approach is presented, conventionally called K-nearest neighbour classifier.
Description: This presentation was presented at Rhodes University
URI: http://hdl.handle.net/10204/3554
Appears in Collections:Logistics and quantitative methods
General science, engineering & technology

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