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Improving the reliability of mixture tuned matched filtering remote sensing classification results using supervised learning algorithms and cross-validation
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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2018 (English)In: Remote Sensing, ISSN 2072-4292, E-ISSN 2072-4292, Vol. 10, no 11, article id 1675Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 10, no 11, article id 1675
Keywords [en]
Accuracy assessment, Hyperspectral remote sensing, Image classification, Leafy spurge, Linear unmixing, Machine learning, Mixture tuned matched filtering (MTMF), Post-processing automation, Supervised learning, Learning systems, Matched filters, Mixtures, NASA, Remote sensing, Space optics, Spectroscopy, Mixture tuned matched filtering, Post processing, Learning algorithms
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Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-247091DOI: 10.3390/rs10111675ISI: 000451733800006Scopus ID: 2-s2.0-85057093302OAI: oai:DiVA.org:kth-247091DiVA, id: diva2:1302294
Note

QC 20190404

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-04-04Bibliographically approved

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Seegmiller, Lindsi

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