Optimal Wavelength Selection of Hyperspectral Scattering Images Based o UVEPLS Projection Analysis

Shuang, 王爽 Wang; Min, 黄敏 Huang; Qibing, 朱启兵 Zhu

doi:10.3788/gzxb20114003.0428

Cited by 3 publications

(1 citation statement)

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“…Various algorithms have been applied to select optimal wavelength, such as the successive projections algorithm (SPA), which has been used to eliminate redundant information in near-infrared spectroscopy in order to analyze the moisture content of wheat [6]. An uninformative viable elimination algorithm (UVE) has also been applied in wavelength selection to establish an apple firmness prediction model [7,8], and a genetic algorithm (GA) was used for wavelength selection in remote sensing hyperspectral systems to improve the observation of surface objects [9,10]. Although these algorithms have been applied for optimal wavelength selection, the accuracy of the results cannot be guaranteed by using these methods in trying to detect the moisture content of vegetable soybeans.…”

Section: Introductionmentioning

confidence: 99%

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Huang

Zhang

et al. 2019

Applied Sciences

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Hyperspectral imaging technology is a promising technique for nondestructive quality evaluation of dried products. In order to realize real-time, online inspection of quality of dried products, it is necessary to determine a few important wavelengths from hyperspectral images for developing a multispectral imaging system. This study presents a binary firework algorithm (BFWA) for selecting the optimal wavelengths from hyperspectral images for moisture evaluation of dried soybean. Hyperspectral images over the spectral region 400–1000 nm, were acquired for 270 dried soybean samples, and mean reflectance was calculated from hyperspectral images for each wavelength. After selecting 12 important wavelengths using BFWA, a moisture prediction model was developed using partial least squares regression (PLSR). The PLSR model with BFWA achieved a prediction accuracy of R p = 0.966 and R M S E P = 5.105 % , which is better than those of successive projections algorithm ( R p = 0.932 and R M S E P = 7.329 % ), and the uninformative viable elimination algorithm ( R p = 0.928 and R M S E P = 7.416 % ). The results obtained by BFWA were more stable, with a smaller standard deviation of R p and R M S E P than those of the genetic algorithm. The BFWA method provides an effective mean for optimal wavelength selection to predict the quality of soybeans during drying.

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Section: Introductionmentioning

confidence: 99%

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Huang

Zhang

et al. 2019

Applied Sciences

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Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress

Chen

Liu

Wang³

et al. 2019

Computers and Electronics in Agriculture

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Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

Wang

Huang

Liu

et al. 2012

2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering

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The typical characteristics of hyperspectral remote sensing data are combining image with spectrum, high spectral resolution, many bands and much redundant information. A hyperspectral image cube can be used to obtain millions spectrum curves. Aiming at a hyperspectral remote sensing image containing huge amounts of data, removing redundant information and reducing processing dimensions are the premise and foundation for hyperspectral remote sensing processing and applications. Hyperspectral data dimension reduction techniques mainly include the feature extraction and band selection. This paper has fully studied the theories and methods of dimension reduction for band selection, analyses their advantages, disadvantages and validity, and deeply discusses the current situation and tendency of the development of band selection based on dimension reduction of hyperspectral remote sensing image at last.

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Optimal Wavelength Selection of Hyperspectral Scattering Images Based o UVEPLS Projection Analysis

Cited by 3 publications

References 0 publications

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Optimal Wavelength Selection for Hyperspectral Imaging Evaluation on Vegetable Soybean Moisture Content during Drying

Estimation of nitrogen and carbon content from soybean leaf reflectance spectra using wavelet analysis under shade stress

Research Advance on Band Selection-Based Dimension Reduction of Hyperspectral Remote Sensing Images

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