Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
This provides high-resolution mapping for environmental monitoring in sparse forest areas, though it is incremental as it builds on existing speckle filtering techniques.
The study tackled the problem of mapping defoliation and regrowth of trees in the tundra-forest ecotone using synthetic aperture radar (SAR) data, achieving over 99.7% classification accuracy with a novel guided nonlocal means speckle filtering method, which matches optical data performance.
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.