基于粗糙集约简的水稻叶片叶绿素含量高光谱反演

doi:10.3969/j.issn.0255-8297.2014.04.009

Abstract

Abstract: 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.

Key words: rough set, vegetation indices reduction, hyperspectral remote sensing, chlorophyll content retrieval, support vector regression (SVR)

CLC Number:

TP399

BI Jing-zhi1, LIU Xiang-nan1, ZHAO Dong2. Chlorophyll Content Retrieval of Rice Canopy with Hyperspectral Inversion Based on Rough Set Reduction[J]. Journal of Applied Sciences, 2014, 32(4): 394-400.

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

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