Remote Sensing Applications and Decision Support

Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine

[+] Author Affiliations
Morteza Shahriari Nia, Daisy Zhe Wang, Paul Gader

University of Florida, Department of Computer and Information Science and Engineering, 412 Newell Drive, Gainesville, Florida 32611, United States

Stephanie Ann Bohlman, Sarah J. Graves

University of Florida, School of Forest Resources and Conservation, 349 Newins-Ziegler Hall Gainesville, Florida 32611, United States

Milenko Petrovic

Institute for Human and Machine Cognition, 15 SE Osceola Avenue, Ocala, Florida 34471, United States

J. Appl. Remote Sens. 9(1), 095990 (Nov 05, 2015). doi:10.1117/1.JRS.9.095990
History: Received June 8, 2015; Accepted October 1, 2015
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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%.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Morteza Shahriari Nia ; Daisy Zhe Wang ; Stephanie Ann Bohlman ; Paul Gader ; Sarah J. Graves, et al.
"Impact of atmospheric correction and image filtering on hyperspectral classification of tree species using support vector machine", J. Appl. Remote Sens. 9(1), 095990 (Nov 05, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.095990


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