论文标题
极化指导的非本地平均值矩阵降解映射的协方差矩阵估计
Polarimetric Guided Nonlocal Means Covariance Matrix Estimation for Defoliation Mapping
论文作者
论文摘要
在这项研究中，我们研究了使用合成的孔径雷达（SAR）数据在苔原 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林 - 森林（Tundra-Forest）中提供高分辨率落叶和再生映射的潜力。使用航拍照片，确定了四个具有活森林的地区和四个带有死树的区域。从同一区域收集了RadarSat-2的四极sAR数据，并使用引导非局部均值斑点滤波的新型扩展计算了复杂的多核偏光协方差矩阵。非局部方法使我们能够保留单程复杂数据的高空间分辨率，这对于精确映射研究区域中稀疏散落的树木至关重要。使用标准的随机森林分类算法，我们的过滤导致超过$ 99.7 \％$分类的精度，高于传统斑点过滤方法，并且基于光学数据的分类精度与分类精度相同。
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.
