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《Transactions of the Chinese Society of Agricultural Engineering》 2015-12
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Improvement of algorithm used for extraction hyperspectral feature bands of vegetation

Cheng Zhiqing;Zhang Jingsong;Meng Ping;Li Yanquan;Wang Hesong;Li Chunyou;Research Institute of Forestry,Chinese Academy of Forestry;Key Laboratory of Tree Breeding and Cultivation,State Forestry Administration;Co-Innovation Center of Sustainable Forestry in Southern China,Nanjing Forestry University;Forestry College,Beijing Forestry University,Key Laboratory of Forest Cultivation and Protection of the Ministry of Education;College of Landscape and Travel,Agricultural University of Hebei;  
Hyperspectral data involve a huge amount of information,and how to select the best combination band to build high-accuracy spectrum model,is the key work for the developing of remote sensing inversion model of vegetation parameter.Maximum correlation coefficient(MCC) is a method of extracting combination bands by using the correlation coefficient between vegetation parameter and hyperspectral band as the selection index of feature combination band.Due to simple calculation and easier operation,MCC is widely used in dimensionality reduction and extracting the useful bands of hyperspectral data.However,the method only considers to extract the maximum correlation bands of vegetation parameter variable,which may lead to overlook the indirect effect of other factors.Optimum index factor(OIF) is the extraction method of feature combination bands based on the basic idea that the amount of information is proportional to the sum of mean square deviation,and inversely proportional to the sum of correlation coefficient for each band.Although OIF can obtain feature combination bands of abundant information and small redundancy,the highest correlation between vegetation parameter and the selected band can't be ensured,which may cause the decreasing of estimation ability of the model which is built by the extraction bands.Obviously,OIF method and MCC method in the extraction of feature combination bands have complementary advantages.In this study,by using the evaluation method with entropy coefficients,OIF and MCC are endowed with objective weight respectively,and based on this the optimal combination band is obtained,which is named as optimum index factor and correlation coefficient(OIFC).The feature combination bands extracted by OIFC,have the highest correlation with the corresponding vegetation parameters,as well as with ensured rich information.In order to demonstrate OIFC's practicality,feature combination bands of winter wheat leaf chlorophyll which was extracted by OIFC were given as an example.The 760,1860 and 1970 nm were considered as feature combination bands and selected by OIFC.Then,hyperspectral model of wheat chlorophyll content was built by partial least squares.Compared with the models from vegetation indices such as normalized difference vegetation index(NDVI) and soil-adjusted vegetation index(SAVI) and OIF method,the precision of the model built by OIFC was the highest and the coefficients of determination was 0.7390.The determination coefficient of linear fitting between predicted values by OIFC and measured chlorophyll contents was 0.827,and the root mean square error was 5.440.The results show that the extracted bands of winter wheat chlorophyll based on OIFC has higher modeling precision.It also proves that OIFC can reliably and effectively extract feature combination bands of vegetation parameters from hyperspectral data.The method of OIFC can also provide theoretical basis and technical support for further improving the accuracy of hyperspectral estimation model of physical and chemical parameters of vegetation.
【Fund】: 林业公益性行业科研专项项目(201204105);; 国家自然科学基金项目(41105076);; 国家重大科学研究计划(2012CB956202)
【CateGory Index】: TP79
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