Jia Kun, Li Qiang-Zi, Tian Yi-Chen, Wu Bing-Fang, Zhang Fei-Fei, Meng Ji-Hua
Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2011 Feb;31(2):483-7.
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.
在本研究中，对用于提高作物光谱分类精度的垂直极化（VV）微波后向散射数据进行了研究。比较了基于HJ卫星多光谱数据与Envisat ASAR VV后向散射数据的融合数据，使用不同分类器的分类精度。结果表明，融合数据能够充分利用HJ多光谱数据的光谱信息以及ASAR VV极化数据的结构敏感性特征。融合数据扩大了不同分类之间的光谱差异，提高了作物分类精度。与单一HJ数据相比，使用融合数据的分类精度可提高5%。此外，ASAR VV极化数据对耕地的非农业区域敏感，加入VV极化数据进行分类能够有效区分田间边界。将VV极化数据与多光谱数据结合用于作物分类，扩大了卫星数据的应用范围，在农业领域具有推广潜力。
