基于粗糙集约简的水稻叶片叶绿素含量高光谱反演

利用高光谱技术可估测水稻冠层叶绿素含量，为水稻的长势遥感监测与农艺决策提供科学依据. 基于地面实测水稻叶片光谱数据，提出了一种粗糙集属性简约和支持向量回归相结合的叶绿素反演方法，解决了植被光谱指数相关性高易造成计算冗余以及降低水稻叶片叶绿素高光谱反演效率的问题. 首先选择18 个与水稻叶绿素含量相关性较大的植被光谱指数作为因变量，利用粗糙集约简植被指数数据空间得到含有6 个植被光谱指数的简约核；然后采用支持向量回归方法反演叶绿素含量. 基于全部指数反演及基于简约核指数反演的R2 分别为0.858 6与0.850 6. 因此，该方法与采用全部指数进行反演的结果相比，不但具有相当的反演精度，而且有效缩短了反演算法步骤及时间，为大数据处理提供了新的技术方法.

Chlorophyll Content Retrieval of Rice Canopy with Hyperspectral Inversion Based on Rough Set Reduction

The rice canopy chlorophyll content can be estimated by using the hyperspectral technique for rice growth monitoring and agronomic decision-making. This paper presents a chlorophyll estimation method based on rough set attribute reduction and support vector regression (SVR) using ground spectral data to solve the problem of data redundancy and low retrieve rate caused by high correlation between vegetation indices. Eighteen hyperspectral indices are selected as variables to estimate the chlorophyll content of rice canopy.The data space is reduced using the rough set algorithm. The SVR algorithm is then introduced to estimate the chlorophyll content. There are six indices reserved in the reduced kernel after attribute reduction. R2 of retrieval results based on all indices and reduced kernel are 0.858 6 and 0.850 6 respectively. The proposed method can achieve an accurate forecasting rate based on all feature attributes, and reduce processing steps and estimation time. It provides a new method for big data processing.