Wavelet Transform (WT) and neural network model applied to canopy hyperspectral data for corn Chl-a estimation in Songnen Plain, China

Dongmei Lu; Kaishan Song; Lin Li; Zongming Wang; Jia Du; Lihong Zeng; Xiaochun Lei

doi:10.1117/12.859690

7 September 2010 Wavelet Transform (WT) and neural network model applied to canopy hyperspectral data for corn Chl-a estimation in Songnen Plain, China

Dongmei Lu, Kaishan Song, Lin Li, Zongming Wang, Jia Du, Lihong Zeng, Xiaochun Lei

Author Affiliations +

Proceedings Volume 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII; 77990P (2010) https://doi.org/10.1117/12.859690
Event: SPIE Optical Engineering + Applications, 2010, San Diego, California, United States

Abstract

In this study, we present spectral measurements of corn chlorophyll content in Changchun (eight times in 2003) and Hailun (five time in 2004), both of which lie in the Songnen Plain, China. Corn canopy reflectance and its derivative reflectance were subsequently used in a linear regression analysis against Chl-a concentration on one by one spectral reflectance. It was found that determination coefficient for Chl-a concentration was high in blue, red and near infrared spectral region, and it was low in green and red edge spectral region, however Chl-a concentration obtained its high determination coefficient in blue, green and red edge spectral region, especially in red edge region with derivative reflectance. Regression models were established based upon 6 spectral vegetation indices and wavelet coefficient, reflectance principal components as well. It was found that wavelet transforms is an effective method of hyperspectral reflectance feature extraction for corn Chl-a estimation, and the best multivariable regressions obtain determination coefficient (R²) up to 0.87 for Chl-a concentration. Finally, neural network algorithms with both specific band reflectance and wavelet coefficient as input variables were applied to estimate corn chlorophyll concentration. The results indicate that estimation accuracy improved with nodes number increasing in the hidden layer, and neural network performs better with wavelet coefficient than that with specific band reflectance as input variables, determination coefficient was up to 0.96 for Chl-a concentration. Further studies are still needed to refine the methods for determining and estimating corn bio-physical/chemical parameters or other vegetation as well in the future.

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Dongmei Lu, Kaishan Song, Lin Li, Zongming Wang, Jia Du, Lihong Zeng, and Xiaochun Lei "Wavelet Transform (WT) and neural network model applied to canopy hyperspectral data for corn Chl-a estimation in Songnen Plain, China", Proc. SPIE 7799, Mathematics of Data/Image Coding, Compression, and Encryption with Applications XII, 77990P (7 September 2010); https://doi.org/10.1117/12.859690

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