RadarSat-2 全极化数据能否应用于土地覆盖分类需要大量的研究和论证。本文以NLCD 为参考数据，利用 SVM 分类器对1 景旧金山地区RadarSat-2 全极化数据进行土地覆盖分类实验，并从分类类别面积一致性、空间相似性两个方 面对分类结果进行分类精度评价。实验获得RadarSat-2 数据分类结果总体分类精度76.91%和kappa 系数0.65，表明RadarSat- 2 全极化数据用于土地覆盖分类分类精度较高，可以达到很好的分类质量。

The full polarization RadarSat-2 data used in land cover classification requires a lot of research and demonstration. Taking NLCD data as reference, this paper uses the SVM to classify 1 scene of RadarSat-2 full polarization data in San Francisco and then evaluates the accuracy of land cover classification results from area of consistency and spatial similarity. Experiments for SAR data classification get the highest overall classification accuracy of 76.91% and kappa coefficient of 0.65, which show that the fully polarization RadarSat-2 data can achieve good classification quality when it is used for land cover classification.