Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping

• Main Authors: Agersborg, Jørgen A., Anfinsen, Stian Normann, Jepsen, Jane Uhd
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2020
• Subjects: Image and Video Processing eess.IV; Machine Learning stat.ML; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; Tundra
• Online Access: https://dx.doi.org/10.48550/arxiv.2001.08976; https://arxiv.org/abs/2001.08976

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. : Update to match final submitted version accepted to IGARSS 2020. 4 pages, 2 columns, 3 figures