高光谱信息的农林植被种类区分

doi:10.3964/j.issn.1000-0593(2018)12-3890-07

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Abstract In order to distinguish between the crops and trees in the main grain producing areas more quickly and accurately, maize, wheat and poplar which are the main vegetation planted in the Huang-Huai-Hai Plain are used as the research object. Obtain the original spectral reflectance and calculate the data by using the original spectral feature point extraction, first derivation, second derivation and vegetation index. Extract the range of feature points, characteristic bands obtained by analyzing the original spectral reflectance, position, amplitude, area and differential value sum in blue, red and yellow edges by first derivation, and vegetation index by empirical formulas. Compare the accuracy of four methods in distinguishing between the three vegetable types based on the principle that the smaller the overlapping range is, the higher the accuracy of parameter will be, and choose the most suitable characteristic index as the identify indicator which has the smallest overlap in different vegetation types. The results showed that among the four methods of manipulation spectral data, first derivation had the highest accuracy in identifying corn, wheat and poplar compared with the original spectral feature point extraction, second derivation and vegetation index. Among the indexes obtained by the first derivation, the amplitude, area and differential value sum in yellow edge region had the higher recognition accuracy. The recognition accuracy of the amplitude in yellow edge was up to 97.5%, and the area and differential value sum in yellow edge was up to 98.1%. The results were verified with 167 other sets of data, and the verification results showed that the recognition accuracy of the amplitude in yellow edge was up to 96.4%, and the area and differential value sum in yellow edge was up to 97.6%. The result was different from that result which was obtained by average reflectance curve of spectrum from the single plant in different growth state, and this method could effectively preserve the difference between individual spectral reflectance curves. Thus, it could be seen that extracting the yellow edge parameters through the first derivation was effectively used in distiguishing vegetation where the crops and trees were planted in the same place, and among all the parameters, area and differential value sum in yellow edge had the highest recognition accuracy.

Key words：Hyperspectral; Vegetation differentiation; Characteristic bands; Range

Received: 2017-10-10 Accepted: 2018-03-05

ZTFLH:

S127

Corresponding Authors: CHENG Zhi-qing E-mail: chengzhiqingl@126.com

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)12-3890-07 OR https://www.gpxygpfx.com/EN/Y2018/V38/I12/3890

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