利用实验区环境星多光谱数据与Envisat ASAR VV极化数据进行融合， 讨论了VV极化微波后向散射数据用于改善多光谱遥感数据农作物分类的精度， 并比较了不同分类方法的分类精度。 结果表明， 两种数据之间的融合充分利用了环境星数据的光谱信息和VV极化数据对于地物结构敏感的特征， 不但增强了不同地物之间的光谱差异， 而且提高了作物分类精度。 两者融合后分类精度比单独使用环境星数据分类精度提高了约5个百分点， 而且由于VV极化数据对田间非耕地信息的敏感性， 对于田块边界的识别效果有很大的改善。 通过该研究探讨了VV极化数据和多光谱数据融合在作物分类中的应用， 拓展了遥感数据在农业领域应用的范围， 具有推广价值。

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.