ABSTRACT

Terrain-induced variations of radar backscatter represent an important limiting factor of many Synthetic Aperture Radar (SAR)-based applications. Radiometric terrain flattening (RTF) is a well-established method that minimizes these variations in SAR imagery. To fully understand the implications of SAR RTF, validation of its impact on the derived products is needed. In this study, we quantified the influence of the RTF on a forest mapping and classification algorithm over Austria, and compared the classification results for the conventional sigma naught and radiometrically terrain-corrected gamma backscatter. The overall accuracy for forest/non-forest mapping and forest type classification improved by 2% and 4%, respectively, over the whole of Austria, with improvements of up to 16% and 20%, respectively, in regions with strong topography.

摘要

地形引起的雷达后向散射变化代表了许多基于合成孔径雷达 (SAR) 的应用的重要限制因素。辐射地形平坦化 (RTF) 是一种行之有效的方法，可以最大限度地减少 SAR 图像中的这些变化。为了充分理解 SAR RTF 的影响，需要验证其对衍生产品的影响。在这项研究中，我们量化了 RTF 对奥地利森林测绘和分类算法的影响，并比较了传统 sigma naught 和辐射测量地形校正伽马后向散射的分类结果。在整个奥地利，森林/非森林测绘和森林类型分类的总体准确度分别提高了 2% 和 4%，在地形强的地区分别提高了 16% 和 20%。