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

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

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 ext...

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Bibliographic Details
Main Authors: Devin Routh, Lindsi Seegmiller, Charlie Bettigole, Catherine Kuhn, Chadwick D. Oliver, Henry B. Glick
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Remote Sensing
Subjects:
mixture tuned matched filtering (MTMF)
image classification
accuracy assessment
post-processing automation
linear unmixing
hyperspectral remote sensing
supervised learning
machine learning
leafy spurge
Online Access:https://www.mdpi.com/2072-4292/10/11/1675

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