| 光谱学与光谱分析 |
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| Research on Identification of Species of Fruit Trees by Spectral Analysis |
| XING Dong-xing1, 2, CHANG Qing-rui1* |
1. College of Environment and Resources, Northwest A & F University, Yangling 712100, China
2. Department of Resources and Environment, Xianyang Normal College, Xianyang 712000, China |
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Abstract Using the spectral reflectance data (Rλ) of canopies, the present paper identifies seven species of fruit trees bearing fruit in the fruit mature period. Firstly, it compares the fruit tree species identification capability of six kinds of satellite sensors and four kinds of vegetation index through re-sampling the spectral data with six kinds of pre-defined filter function and the related data processing of calculating vegetation indexes. Then, it structures a BP neural network model for identifying seven species of fruit trees on the basis of choosing the best transformation of Rλ and optimizing the model parameters. The main conclusions are: (1) the order of the identification capability of the six kinds of satellite sensors from strong to weak is: MODIS, ASTER, ETM+, HRG, QUICKBIRD and IKONOS; (2) among the four kinds of vegetation indexes, the identification capability of RVI is the most powerful, the next is NDVI, while the identification capability of SAVI or DVI is relatively weak; (3) The identification capability of RVI and NDVI calculated with the reflectance of near-infrared and red channels of ETM+ or MODIS sensor is relatively powerful; (4) Among Rλ and its 22 kinds of transformation data, d1[log(1/Rλ)] (derivative gap is set 9 nm) is the best transformation for structuring BP neural network model; (5) The paper structures a 3-layer BP neural network model for identifying seven species of fruit trees using the best transformation of Rλ which is d1[log(1/Rλ)] (derivative gap is set 9 nm).
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Received: 2008-05-08
Accepted: 2008-08-12
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Corresponding Authors:
CHANG Qing-rui
E-mail: changqr@nwsuaf.edu.cn
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