Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 9-13, 2016
https://doi.org/10.5194/isprs-archives-XLI-B1-9-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
02 Jun 2016
WAVELENGTH SELECTION OF HYPERSPECTRAL LIDAR BASED ON FEATURE WEIGHTING FOR ESTIMATION OF LEAF NITROGEN CONTENT IN RICE
Lin Du1,2, Shuo Shi2,3, Wei Gong2,3, Jian Yang2, Jia Sun2, and Feiyue Mao4 1School of Physics and Technology, Wuhan University, Wuhan, China
2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
3Collaborative Innovation Centre of Geospatial Technology, Wuhan, China
4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Keywords: Hyperspectral LiDAR, Feather Weighting, Leaf Nitrogen Content, Wavelength Selection, Partial Least Squares, Support Vector Machine Abstract. Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.
Conference paper (PDF, 866 KB)


Citation: Du, L., Shi, S., Gong, W., Yang, J., Sun, J., and Mao, F.: WAVELENGTH SELECTION OF HYPERSPECTRAL LIDAR BASED ON FEATURE WEIGHTING FOR ESTIMATION OF LEAF NITROGEN CONTENT IN RICE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 9-13, https://doi.org/10.5194/isprs-archives-XLI-B1-9-2016, 2016.

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