Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping

• URL: http://arxiv.org/abs/2001.08976v2
• Date: Fri, 17 Jul 2020 09:12:32 GMT
• Title: Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
• Authors: J{\o}rgen A. Agersborg, Stian Normann Anfinsen and Jane Uhd Jepsen
• Abstract summary: We investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four areas with live forest and four areas with dead trees were identified. Our filtering results in over $99.7 %$ classification accuracy, higher than traditional speckle filtering methods, and on par with the classification accuracy based on optical data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study we investigate the potential for using synthetic aperture radar (SAR) data to provide high resolution defoliation and regrowth mapping of trees in the tundra-forest ecotone. Using aerial photographs, four areas with live forest and four areas with dead trees were identified. Quad-polarimetric SAR data from RADARSAT-2 was collected from the same area, and the complex multilook polarimetric covariance matrix was calculated using a novel extension of guided nonlocal means speckle filtering. The nonlocal approach allows us to preserve the high spatial resolution of single-look complex data, which is essential for accurate mapping of the sparsely scattered trees in the study area. Using a standard random forest classification algorithm, our filtering results in over $99.7 \%$ classification accuracy, higher than traditional speckle filtering methods, and on par with the classification accuracy based on optical data.

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