Development and application of normalized spectral index based on hyperspectral imagery classification

Zhang Dongyan; Zhao Jinling; Huang Linsheng; Ma Wenqiu

Volume 43 Issue 2

Mar. 2014

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Article Navigation > Infrared and Laser Engineering > 2014 > 43(2): 586-594

Zhang Dongyan, Zhao Jinling, Huang Linsheng, Ma Wenqiu. Development and application of normalized spectral index based on hyperspectral imagery classification[J]. Infrared and Laser Engineering, 2014, 43(2): 586-594.

Citation:

Zhang Dongyan, Zhao Jinling, Huang Linsheng, Ma Wenqiu. Development and application of normalized spectral index based on hyperspectral imagery classification[J]. Infrared and Laser Engineering, 2014, 43(2): 586-594.

Development and application of normalized spectral index based on hyperspectral imagery classification

1.
Key Laboratory of Intelligent Computing & Signal Processing,Ministry of Education,Hefei 230039,China;
2.
Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China

Received Date: 2013-06-20
Rev Recd Date: 2013-07-25
Publish Date: 2014-02-25

Abstract

Near-ground imaging spectroscopy applied in field provides new opportunity for development of quantitative remote sensing in agriculture. It deserves concern about how to exert its data advantage of integrating image and spectra into one, particularly in analyzing the influence of background targets, such as soil, shadow on crop nutrient inversion model. In this research, imaging cubes of wheat group in the field were collected by visible/near-infrared imaging spectrometer (VNIS). A normalized spectral index was set up according to reflectance characteristics of illuminated soil, shadow soil, illuminated leaf and shadow leaf in the image. Furthermore, the index was used to extract spectra of different targets in soybean images and analyze the variation of determination coefficient R2 between normalized spectra of soybean group and chlorophyll density before and after removing background soil. The results showed that when spectra of soil and shadow leaf were removed, the sensitive bands of chlorophyll density shifted from red and near-infrared ranges (727 nm, 922 nm) to red ranges (710 nm, 711 nm), meanwhile, the overall trend was that visible ranges increased, near-infrared regions decreased and red bands had the highest determination coefficient. Therefore, it can be concluded that spectral purification based on normalized spectral index has important significance for quantitative research in agricultural remote sensing.
- hyperspectral imaging,
- normalized spectral index,
- imagery classification,
- spectrum purification,
- wheat,
- soybean

References

[1]
[2]	Rogan John, Jennifer Miller, Doug Stow, et al. Land-Cover change monitoring with classification trees using landsat tm and ancillary data[J]. Photogrammetric Engineering Remote Sensing, 2003, 69 (7): 793-804.
[3]
[4]	Gong P, Pu R, Biging G S, et al. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1355-1362.
[5]	Boggs G　S. Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 217-224.
[6]
[7]
[8]	Zhang X, Feng X, Jiang H. Object-oriented method for urban vegetation mapping using IKONOS imagery[J]. International Journal of Remote Sensing, 2010, 31(1): 177-196.
[9]	Yang M, Zhao C, Zhao Y, et al. Research on a method to derive wheat canopy information from airborne imaging spectrometer data[J]. Scientia Agricultura Sinica, 2002, 35(6): 626-631.
[10]
[11]
[12]	Kumara A S, Keerthi V, Manjunath A　S, et al. Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 261-269.
[13]
[14]	Martin D　P, Rybicki E　P. Microcomputer-based quantification of maize streak virus symptoms in Zea mays[J]. Phytopathology, 1998, 88(5): 422-427.
[15]
[16]	Lukina E　V, Stone M　L, Raun W　R. Estimating vegetation coverage in wheat using digital images[J]. Journal of Plant Nutrition, 1999, 22(2): 341-350.
[17]
[18]	Ahmad I　S, Reid J　F, Paulsen M　R, et al. Color classifier for symptomatic soybean seeds using image processing[J]. Plant Disease, 1999, 83(4): 320-327.
[19]	Shi Y, Deng J, Chen L, et al. Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique[J]. Spectroscopy and Spectral Analysis, 2010, 30(1): 214-219.
[20]
[21]
[22]	Scharf P C, Lory J A. Calibrating corn color from aerial photographs to predict sidedress N need [J]. Agronomy Journal, 2002, 94: 397-404.
[23]
[24]	Jia L, Chen X, Zhang F, et al. To detect nitrogen status of inter wheat by using color digital camera [J]. J Plant Nutrition, 2004, 27(3): 441-450.
[25]
[26]	Tong Q, Xue Y, Wang J, et al. Development and application of the field imaging spectrometer system [J]. Journal of Remote Sensing, 2010, 14(3): 409-422.
[27]	Zhang D Y, Liu L Y, Huang W J, et al. Inversion and evaluation of crop chlorophyll density based on analyzing image and spectrum[J]. Infrared and Laser Engineering, 2013, 42(7): 1871-1181.
[28]
[29]	Christian Nansen, Tulio Macedo, Rand Swanson, et al. Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants [J]. International Journal of Remote Sensing, 2009, 30(10):2447-2464.
[30]
[31]	Chai A, Liao N, Tian L. Identification of cucumber disease using hyperspectral imaging and discriminate analysis[J]. Spectroscopy and Spectral Analysis, 2010, 30(5): 1357-1361.
[32]
[33]
[34]	Inoue Y, Penuelas J. An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications[J]. International Journal of Remote Sensing, 2001, 22(18): 3883-3888.
[35]	Tan H, Li S, Wang K, et al. Monitoring canopy chlorophyll density in seedlings of winter wheat using imaging spectrometer[J]. Acta Agronomica Sinica, 2008, 34(10):　1812-1817.
[36]
[37]	Zhang L, Huang C, Wu T, et al. Laboratory calibration of a field imaging spectrometer system [J]. Sensors, 2011, 11:2408-2425.
[38]
[39]
[40]	Huang W. Remote sensing monitoring of crops diseases mechanism and application[J]. China's Agricultural Science and Technology Press, 2009: 18-24.
[41]	Wang J, Zhao C, Huang W. Basis and Application of Quantitative Remote Sensing in Agriculture[M]. Beijing:　Science Press, 2008: 5-11.
[42]
[43]	Yu B I, Michael Ostland, Peng Gong. Penalized discriminant analysis of in situ hperspectral data for conifer species recognition[J]. IEEE Transactions on Geosciences and Remote Sensing, 1999, 5(37): 2569-2576.
[44]
[45]
[46]	Pu R. Broadleaf species recognition with in situ hyperspectral data[J]. International Journal of Remote Sensing, 2009, 30:　2759-2779.
[47]
[48]	Zhang D Y, Liang D, Zhao J L, et al. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3): 787-797.
[49]
[50]	Haboudane D, Tremblay N, Miller J R, et al. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspecral data[J]. IEEE Transaction on Geoscience and Remote sensing, 2008, 46: 423-427.
[51]
[52]	Gitelson A A, Kaufman Y, Merzlyak M. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298.
[53]	Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003, 86: 542-553.
[54]
[55]	Roshanak Darvishzadeh, Andrew Skidmore, Artin Chlerf, et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in heterogeneous grassland[J]. Remote Sensing of Environment, 2008, 112: 2592-2604.
[56]
[57]	Fig.4 Identified result of imagery classification

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Development and application of normalized spectral index based on hyperspectral imagery classification

1. Key Laboratory of Intelligent Computing & Signal Processing,Ministry of Education,Hefei 230039,China;

2. Beijing Research Center for Information Technology in Agriculture,Beijing 100097,China

Received Date: 2013-06-20

Rev Recd Date: 2013-07-25

Publish Date: 2014-02-25

Key words:

Abstract: Near-ground imaging spectroscopy applied in field provides new opportunity for development of quantitative remote sensing in agriculture. It deserves concern about how to exert its data advantage of integrating image and spectra into one, particularly in analyzing the influence of background targets, such as soil, shadow on crop nutrient inversion model. In this research, imaging cubes of wheat group in the field were collected by visible/near-infrared imaging spectrometer (VNIS). A normalized spectral index was set up according to reflectance characteristics of illuminated soil, shadow soil, illuminated leaf and shadow leaf in the image. Furthermore, the index was used to extract spectra of different targets in soybean images and analyze the variation of determination coefficient R2 between normalized spectra of soybean group and chlorophyll density before and after removing background soil. The results showed that when spectra of soil and shadow leaf were removed, the sensitive bands of chlorophyll density shifted from red and near-infrared ranges (727 nm, 922 nm) to red ranges (710 nm, 711 nm), meanwhile, the overall trend was that visible ranges increased, near-infrared regions decreased and red bands had the highest determination coefficient. Therefore, it can be concluded that spectral purification based on normalized spectral index has important significance for quantitative research in agricultural remote sensing.

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Reference (57)

[1]
[2]	Rogan John, Jennifer Miller, Doug Stow, et al. Land-Cover change monitoring with classification trees using landsat tm and ancillary data[J]. Photogrammetric Engineering Remote Sensing, 2003, 69 (7): 793-804.
[3]
[4]	Gong P, Pu R, Biging G S, et al. Estimation of forest leaf area index using vegetation indices derived from hyperion hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1355-1362.
[5]	Boggs G　S. Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 217-224.
[6]
[7]
[8]	Zhang X, Feng X, Jiang H. Object-oriented method for urban vegetation mapping using IKONOS imagery[J]. International Journal of Remote Sensing, 2010, 31(1): 177-196.
[9]	Yang M, Zhao C, Zhao Y, et al. Research on a method to derive wheat canopy information from airborne imaging spectrometer data[J]. Scientia Agricultura Sinica, 2002, 35(6): 626-631.
[10]
[11]
[12]	Kumara A S, Keerthi V, Manjunath A　S, et al. Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: 261-269.
[13]
[14]	Martin D　P, Rybicki E　P. Microcomputer-based quantification of maize streak virus symptoms in Zea mays[J]. Phytopathology, 1998, 88(5): 422-427.
[15]
[16]	Lukina E　V, Stone M　L, Raun W　R. Estimating vegetation coverage in wheat using digital images[J]. Journal of Plant Nutrition, 1999, 22(2): 341-350.
[17]
[18]	Ahmad I　S, Reid J　F, Paulsen M　R, et al. Color classifier for symptomatic soybean seeds using image processing[J]. Plant Disease, 1999, 83(4): 320-327.
[19]	Shi Y, Deng J, Chen L, et al. Leaf characteristics extraction of rice under potassium stress based on static scan and spectral segmentation technique[J]. Spectroscopy and Spectral Analysis, 2010, 30(1): 214-219.
[20]
[21]
[22]	Scharf P C, Lory J A. Calibrating corn color from aerial photographs to predict sidedress N need [J]. Agronomy Journal, 2002, 94: 397-404.
[23]
[24]	Jia L, Chen X, Zhang F, et al. To detect nitrogen status of inter wheat by using color digital camera [J]. J Plant Nutrition, 2004, 27(3): 441-450.
[25]
[26]	Tong Q, Xue Y, Wang J, et al. Development and application of the field imaging spectrometer system [J]. Journal of Remote Sensing, 2010, 14(3): 409-422.
[27]	Zhang D Y, Liu L Y, Huang W J, et al. Inversion and evaluation of crop chlorophyll density based on analyzing image and spectrum[J]. Infrared and Laser Engineering, 2013, 42(7): 1871-1181.
[28]
[29]	Christian Nansen, Tulio Macedo, Rand Swanson, et al. Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants [J]. International Journal of Remote Sensing, 2009, 30(10):2447-2464.
[30]
[31]	Chai A, Liao N, Tian L. Identification of cucumber disease using hyperspectral imaging and discriminate analysis[J]. Spectroscopy and Spectral Analysis, 2010, 30(5): 1357-1361.
[32]
[33]
[34]	Inoue Y, Penuelas J. An AOTF-based hyperspectral imaging system for field use in ecophysiological and agricultural applications[J]. International Journal of Remote Sensing, 2001, 22(18): 3883-3888.
[35]	Tan H, Li S, Wang K, et al. Monitoring canopy chlorophyll density in seedlings of winter wheat using imaging spectrometer[J]. Acta Agronomica Sinica, 2008, 34(10):　1812-1817.
[36]
[37]	Zhang L, Huang C, Wu T, et al. Laboratory calibration of a field imaging spectrometer system [J]. Sensors, 2011, 11:2408-2425.
[38]
[39]
[40]	Huang W. Remote sensing monitoring of crops diseases mechanism and application[J]. China's Agricultural Science and Technology Press, 2009: 18-24.
[41]	Wang J, Zhao C, Huang W. Basis and Application of Quantitative Remote Sensing in Agriculture[M]. Beijing:　Science Press, 2008: 5-11.
[42]
[43]	Yu B I, Michael Ostland, Peng Gong. Penalized discriminant analysis of in situ hperspectral data for conifer species recognition[J]. IEEE Transactions on Geosciences and Remote Sensing, 1999, 5(37): 2569-2576.
[44]
[45]
[46]	Pu R. Broadleaf species recognition with in situ hyperspectral data[J]. International Journal of Remote Sensing, 2009, 30:　2759-2779.
[47]
[48]	Zhang D Y, Liang D, Zhao J L, et al. Bidirectional reflectance characteristics of soybean canopy using multi-angle hyperspectral imaging[J]. Infrared and Laser Engineering, 2013, 42(3): 787-797.
[49]
[50]	Haboudane D, Tremblay N, Miller J R, et al. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspecral data[J]. IEEE Transaction on Geoscience and Remote sensing, 2008, 46: 423-427.
[51]
[52]	Gitelson A A, Kaufman Y, Merzlyak M. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58(3): 289-298.
[53]	Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression[J]. Remote Sensing of Environment, 2003, 86: 542-553.
[54]
[55]	Roshanak Darvishzadeh, Andrew Skidmore, Artin Chlerf, et al. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in heterogeneous grassland[J]. Remote Sensing of Environment, 2008, 112: 2592-2604.
[56]
[57]	Fig.4 Identified result of imagery classification

Development and application of normalized spectral index based on hyperspectral imagery classification

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References

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通讯作者: 陈斌, bchen63@163.com

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