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Remote Sens. 2018, 10(11), 1675; https://doi.org/10.3390/rs10111675

Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

1
Department of Environmental Systems Science, ETH Zürich, 8092 Zürich, Switzerland
2
Division of Geoinformatics, Department of Urban Planning and Environment, KTH Royal Institute of Technology, SE-114 28 Stockholm, Sweden
3
School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA
4
School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
Joint first authorship.
*
Author to whom correspondence should be addressed.
Received: 22 August 2018 / Revised: 30 September 2018 / Accepted: 16 October 2018 / Published: 23 October 2018
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Abstract

Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies. View Full-Text
Keywords: mixture tuned matched filtering (MTMF); image classification; accuracy assessment; post-processing automation; linear unmixing; hyperspectral remote sensing; supervised learning; machine learning; leafy spurge mixture tuned matched filtering (MTMF); image classification; accuracy assessment; post-processing automation; linear unmixing; hyperspectral remote sensing; supervised learning; machine learning; leafy spurge
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Routh, D.; Seegmiller, L.; Bettigole, C.; Kuhn, C.; Oliver, C.D.; Glick, H.B. Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation. Remote Sens. 2018, 10, 1675.

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