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Journal of Applied Remote Sensing

Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine
Author(s): Morteza Shahriari Nia; Daisy Zhe Wang; Stephanie Ann Bohlman; Paul D. Gader; Sarah J. Graves; Milenko Petrovic
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Paper Abstract

Hyperspectral images can be used to identify savannah tree species at the landscape scale, which is a key step in measuring biomass and carbon, and tracking changes in species distributions, including invasive species, in these ecosystems. Before automated species mapping can be performed, image processing and atmospheric correction is often performed, which can potentially affect the performance of classification algorithms. We determine how three processing and correction techniques (atmospheric correction, Gaussian filters, and shade/green vegetation filters) affect the prediction accuracy of classification of tree species at pixel level from airborne visible/infrared imaging spectrometer imagery of longleaf pine savanna in Central Florida, United States. Species classification using fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) atmospheric correction outperformed ATCOR in the majority of cases. Green vegetation (normalized difference vegetation index) and shade (near-infrared) filters did not increase classification accuracy when applied to large and continuous patches of specific species. Finally, applying a Gaussian filter reduces interband noise and increases species classification accuracy. Using the optimal preprocessing steps, our classification accuracy of six species classes is about 75%.

Paper Details

Date Published: 5 November 2015
PDF: 14 pages
J. Appl. Remote Sens. 9(1) 095990 doi: 10.1117/1.JRS.9.095990
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Morteza Shahriari Nia, Univ. of Florida (United States)
Daisy Zhe Wang, Univ. of Florida (United States)
Stephanie Ann Bohlman, Univ. of Florida (United States)
Paul D. Gader, Univ. of Florida (United States)
Sarah J. Graves, Univ. of Florida (United States)
Milenko Petrovic, Florida Institute for Human & Machine Cognition (United States)

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