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- 实验方案
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[IEEE 2019 International Conference on Advanced Electrical Engineering (ICAEE) - Algiers, Algeria (2019.11.19-2019.11.21)] 2019 International Conference on Advanced Electrical Engineering (ICAEE) - Study of Hybrid Photovoltaic/Thermal Collector Provided With Finned Metal Plates: A Numerical Investigation under Real Operating Conditions
摘要： This paper investigates at the example of bathymetry how much an application can profit from comprehensive characterizations required for an improved calibration of data from a state-of-the-art commercial hyperspectral sensor. A NEO HySpex VNIR-1600 sensor is used for this paper, and the improvements are based on measurements of sensor properties not covered by the manufacturer, in particular, detector nonlinearity and stray light. This additional knowledge about the instrument is used to implement corrections for nonlinearity, stray light, spectral smile distortion and nonuniform spectral bandwidth and to base the radiometric calibration on a SI-traceable radiance standard. Bathymetry is retrieved from a data take from the lake Starnberg using WASI-2D. The results using the original and improved calibration procedures are compared with ground reference data, with an emphasis on the effect of stray-light correction. For our instrument, stray-light biases the detector response from 416–500 nm up to 8% and from 700–760 nm up to 5%. Stray-light-induced errors affect bathymetry mainly in water deeper than Secchi depth, whereas in shallower water, the dominant error source is the calibration accuracy of the light source used for radiometric calibration. Stray-light correction reduced the systematic error of water depth by 19% from Secchi depth to three times Secchi depth, whereas the relative standard deviation remained stable at 5%.
关键词： stray light,Bathymetry,calibration,nonlinearity,remote sensing,hyperspectral,imaging spectrometer
更新于2025-09-16 10:30:52
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Deriving spectral information upon the laser welding process employing a hyperspectral imaging technique
摘要： In this paper we present results from process observations of deep penetration laser welding using a hyperspectral imaging (HSI) technique. For monitoring different types of laser-based material processing, we developed an appropriate high-speed camera-based HSI system. This system enables us to derive spectra of the deep penetration welding process with high time resolution. These spectra can be used for temperature determination or to extract spectral characteristics, both of which can help to further investigate process dynamics and properties of the vapor. The presented results contain particularly HSI-derived spectral information of the evolving vapor plume that is related to corresponding high-speed images and spectra acquired with a conventional VIS-spectrometer.
关键词： laser-based material processing,deep penetration laser welding,process monitoring,hyperspectral imaging
更新于2025-09-12 10:27:22
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Fourier transform infrared imaging and quantitative analysis of pre-treated wood fibers: A comparison between partial least squares and multivariate curve resolution with alternating least squares methods in a case study
摘要： Pretreated lignocellulosic fibers were used as a case study to compare two chemometric methods for the quantification of chemical components in Fourier transformed infrared (FT-IR) images. Partial least squares (PLS) and multivariate curve resolution with alternating least squares (MCR-ALS) methods were applied to the images to quantify glucans, lignin and hemicellulose content. The main problem for calibration in samples from natural origin is to obtain proper reference material for pixel to pixel quantification. Furthermore, chemical components in wood experience changes after different pretreatment conditions; therefore commercially available reference material may not have the same identity of the components present in the sample. Concentration information of bulk samples obtained by wet chemistry methods, along with the median spectrum of whole images, was used as an alternative for PLS calibration in this scenario. Results show that both methods provided similar spatial distribution for lignin and hemicellulose in the concentration maps, but image reconstruction of glucans shows differences in distribution between the two methods. PLS models used to quantify pixels in an image were previously validated through the prediction of global concentration of samples, using the median spectrum of different images (RMSEP ? 1.3% for glucans, 1.0% for lignin and 0.9% for hemicelluloses); The range of pixel concentration predicted in a single image was too narrow possibly due to the lack of a calibration set with a wider dynamic range. Concentration maps obtained with MCR-ALS were satisfactory and the range of concentration for pixels was more consistent with what would be expected. A quantification approach that does not need a calibration set was used to transform concentration profiles into real concentration units for pixels. Therefore MCR-ALS was a more suitable method for quantification in this specific case study.
关键词： PLS,Lignocellulosic fibers,Hyperspectral imaging,Quantitative analysis,MCR-ALS
更新于2025-09-12 10:27:22
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[IEEE 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Nagoya, Japan (2019.7.13-2019.7.15)] 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) - Salt Content Prediction System of Dried Sea Cucumber (Beche-de-mer) Based on Visual Near-Infrared Imaging
摘要： Dried sea cucumber (Beche-de-mer) is a culinary food that is considered luxurious and delicious, especially in China, Korea, and Japan, so the price is quite high. Dried sea cucumber (Beche-de-mer) also has high commercial value and high nutritional value. Their quality determines dried sea cucumber (Beche-de-mer) prices on international markets. One of the parameters that determine its quality is salt content. The excessive salt content in Dried sea cucumber (Beche-de- mer) can cause health problems such as hypertension, stroke, digestive system disorders, etc. Therefore, this paper will discuss a prediction system for measuring salt content in Dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with a wavelength from 400 to1000 nm. The hardware from the prediction system for measuring salt content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon tables, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of salt content. Then the results of the prediction model are compared with the data references obtained by the mercury nitrate method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of salt content. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.27, respectively, with the number of PLS component is 25. Based on the results of this work, the proposed system can be used as an alternative method of measuring the salt content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability.
关键词： Hyperspectral Imaging,Salt Content,Dried Sea Cucumber,PLSR (Partial Least Square Regression)
更新于2025-09-12 10:27:22
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Spectral Reshaping of Single Dye Molecules Coupled to Single Plasmonic Nanoparticles
摘要： Fluorescent molecules are highly susceptible to their local environment. Thus, a fluorescent molecule near a plasmonic nanoparticle can experience changes in local electric field and local density of states that reshape its intrinsic emission spectrum. By avoiding ensemble averaging while simultaneously measuring the super-resolved position of the fluorophore and its emission spectrum, single-molecule hyperspectral imaging is uniquely suited to differentiate changes in spectrum from heterogeneous ensemble effects. Thus, we uncover for the first time single-molecule fluorescence emission spectrum reshaping upon near-field coupling to individual gold nanoparticles using hyperspectral super-resolution fluorescence imaging, and we resolve this spectral reshaping as a function of the nanoparticle/dye spectral overlap and separation distance. We find dyes bluer than the plasmon resonance maximum are red-shifted and redder dyes are blue-shifted. The primary vibronic peak transition probabilities shift to favor secondary vibronic peaks, leading to effective emission maxima shifts in excess of 50 nm, and we understand these light-matter interactions by combining super-resolution hyperspectral imaging and full-field electromagnetic simulations.
关键词： Plasmonic nanoparticles,Single-molecule hyperspectral imaging,Optoelectronics,Energy Conversion and Storage,Fluorescence emission spectrum reshaping,Plasmonics
更新于2025-09-11 14:15:04
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[IEEE 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Munich, Germany (2019.6.23-2019.6.27)] 2019 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC) - Hyperspectral Imaging of Bio-Inspired Tilted Cholesteric Liquid Crystal Structures
摘要： The cholesteric liquid crystal (CLC) phase exhibits a helical structure with a twist axis perpendicular to the local molecular director. When light propagates in the Bragg regime through a CLC slab with a planar texture, the medium gives rise to Bragg reflection, selective in wavelength and in polarization. The characteristics of the Bragg band are tunable by acting on the following structural parameters: the pitch, the helicity sense and the helix orientation. Tuning the latter parameter produces polygonal textures (Fig. 1, center). Such a texture is made of an array of micrometer-scale polygonal cells, acting a network of microlenses with wavelength-tunable focusing properties [1]. Polygonal textures can be found in biological CLCs (for instance, multiwavelength micromirrors the cuticle of scarab beetle Chrysina gloriosa [2]). Here, we perform jointly spectral and spatial characterization of CLCs with oblique helicity through a liquid-crystal-based hyperspectral imaging (HSI), which is a compact and non-destructive technique ideally adapted to characterize mesoscopic samples, both in transmission and reflection [3]. Although liquid crystal have already been used to perform multispectral and hyperspectral imaging, they have not been, to date, the subject of an HSI study. The instrument output is an image with 512*128 pixels, each of them being spectrally resolved. Spectro-spatial properties of the polygonal texture are then measured with unprecedented spectral resolution for tilted CLCs, that is 6 nm over 400-1000nm, while conventional multispectral imaging is limited to a spectral resolution of a few tens of nanometers. Our experimental results are summarized in Fig. 1. In transmission, the mesoscopic chromatic pattern of the studied sample is recovered, and its wavelength-tunable light shaping properties are emphasized with the reconstruction of the hyperspectral datacube of a quarter of polygon. Furthermore, the reflected light analysis of a single polygon (lateral dimension below 15μm) reveals the local tilting of the CLC helical axis, that is the bulk distortion constituting the texture. The signature resides in the fine tuning of the spectral characteristics of the bandgap into the part of a polygon in which the orientation of the helix axis is spatially changing. A correlation between spatial changes and spectral changes on a mesoscopic scale is therefore made possible [4]. These results demonstrate the interest of hyperspectral imaging for the precise study of complex cholesteric components, also existing in biological samples. It thus opens many perspectives to enrich and deepen our knowledge on the fine structure of these materials. Additionally, it is found that polygonal textures may offer practical solutions in numerous applications, from secure authentication to information-enriched imaging, and, particularly, in the field of cryptography.
关键词： wavelength-tunable,Bragg reflection,polygonal textures,cholesteric liquid crystal,hyperspectral imaging
更新于2025-09-11 14:15:04
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[IEEE 2019 6th International Conference on Instrumentation, Control, and Automation (ICA) - Bandung, Indonesia (2019.7.31-2019.8.2)] 2019 6th International Conference on Instrumentation, Control, and Automation (ICA) - Moisture Content Prediction System of Dried Sea Cucumber (Beche-de-mer) Based on Visual Near-Infrared Imaging
摘要： Dried sea cucumber (Beche-de-mer), the product after cleaning, boiling, salting, and drying, is as delicious and healthy food. Dried sea cucumber (Beche-de-mer) also has a high market price and the highest nutritional value of all seafood products. Moisture content in dried sea cucumber (Beche-de-mer) can affect the international market prices of dried sea cucumber to decline. This condition takes place because the moisture content is one of the parameters that determine the quality of dried sea cucumber. Therefore, this research will discuss a prediction system for measuring moisture content in dried sea cucumber (Beche-de-mer) using hyperspectral imaging technique. This system uses reflectance mode with the wavelength from 400 to 1000 nm. The hardware from the prediction system for measuring moisture content is motors to generate, hyperspectral camera system, two 150 W halogen lamps, Teflon table, and personal computer link. Then, the PLSR algorithm is applied to the prediction system model at full wavelength. The prediction model is used to obtain the predicted value of moisture content. Then the results of the prediction model are compared with the data references obtained by the gravimetric method. The root means square errors and correlation coefficient are used to evaluate the prediction system performance of moisture content prediction. The best result of the prediction system in this work is to have a correlation coefficient of 0.99 and root mean square errors of 0.92% respectively, with the number of PLS component is 30. Based on the results of this research, the proposed system can be used as an alternative method of measuring the moisture content in dried sea cucumber (Beche-de-mer) with excellent accuracy and high reliability
关键词： Hyperspectral Imaging,Moisture content,Partial Least Square Regression,Dried Sea Cucumber (Beche-de-mer)
更新于2025-09-11 14:15:04
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Qualitative and quantitative diagnosis of nitrogen nutrition of tea plants under field condition using hyperspectral imaging coupled with chemometrics
摘要： BACKGROUND: Rapid and accurate diagnosis of nitrogen (N) status in ?eld crops is of great signi?cance for site-speci?c N fertilizer management. This study aimed to evaluate the potential of hyperspectral imaging coupled with chemometrics for the qualitative and quantitative diagnosis of N status in tea plants under ?eld conditions. RESULTS: Hyperspectral data from mature leaves of tea plants with di?erent N application rates were preprocessed by standard normal variate (SNV). Partial least squares discriminative analysis (PLS-DA) and least squares–support vector machines (LS-SVM) were used for the classi?cation of di?erent N status. Furthermore, partial least squares regression (PLSR) was used for the prediction of N content. The results showed that the LS-SVM model yielded better performance with correct classi?cation rates of 82% and 92% in prediction sets for the diagnosis of di?erent N application rates and N status, respectively. The PLSR model for leaf N content (LNC) showed excellent performance, with correlation coe?cients of 0.924, root mean square error of 0.209, and residual predictive deviation of 2.686 in the prediction set. In addition, the important wavebands of the PLSR model were interpreted based on regression coe?cients. CONCLUSION: Overall, our results suggest that the hyperspectral imaging technique can be an e?ective and accurate tool for qualitative and quantitative diagnosis of N status in tea plants.
关键词： nitrogen status,hyperspectral imaging,leaf nitrogen content,tea plant
更新于2025-09-11 14:15:04
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Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data
摘要： Mapping land and aquatic vegetation of coastal areas using remote sensing for better management and conservation has been a long-standing interest in many parts of the world. Due to natural complexity and heterogeneity of vegetation cover, various remote sensing sensors and techniques are utilized for monitoring coastal ecosystems. In this study, two unsupervised and two supervised standard pixel-based classifiers were tested to evaluate the mapping performance of the second-generation airborne NASA Glenn Hyperspectral Imager (HSI2) over the narrow coastal area along the Western Lake Erie’s shoreline. Furthermore, the classification results of HSI2 (using the whole Visible-Near Infrared (VIS+ NIR) hyperspectral dataset, and also the spectral subset of Visible (VIS) spectral bands) were compared to multispectral Pleiades (VIS+ NIR) and Unmanned Aerial Vehicle (UAV) VIS classified images. The goal was to explore how different spectral ranges, and spatial and spectral resolutions impact the unsupervised and supervised classifiers. While the unsupervised classifiers depended more on the spectral range, spectral or spatial resolutions were important for the supervised classifiers. The Support Vector Machine (SVM) was found to perform better than other classification methods for the HSI2 images over all twenty-two study sites with the overall accuracy (OA) ranging from 82.6%–97.5% for VIS, and 81.5%–95.6 % for VIS + NIR. Considerably better performance of the supervised classifiers for the HSI2 VIS data over the Pleiades data (OA = 74.8–83.4%) suggested the importance of spectral resolution over spectral range (VIS vs. VIS+ NIR) for the supervised methods. The unsupervised classifiers exhibited low accuracy for both HSI2 VIS and UAV VIS imagery (OA< 30.0%) while the overall accuracy for the HSI2 VIS+ NIR and Pleiades data ranged from 60.4%–78.4 % and 42.1%–66.4%, respectively, suggesting the importance of spectral range for the unsupervised classifiers.
关键词： Lake Erie,UAV,HSI2,Pleiades,Remote sensing,Vegetation classification,Hyperspectral imaging
更新于2025-09-11 14:12:44
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Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging
摘要： Hyperspectral imaging is becoming an increasingly popular tool for high-throughput plant phenotyping, because it provides remarkable insights about the health status of plants. Feature selection is a key component in a hyperspectral image analysis, largely because a significant portion of spectral features are redundant and/or irrelevant, depending on the desired application. This paper presents an ensemble feature selection method to identify the most informative spectral features for practical applications in plant phenotyping. The hyperspectral data set contained the images of four wheat lines, each with a control and a salt (NaCl) treatment. To rank spectral features, six feature selection methods were used as the base for the ensemble: correlation-based feature selection, ReliefF, sequential feature selection, support vector machine-recursive feature elimination (SVM-RFE), LASSO logistic regression, and random forest. The best results were achieved by the ensemble of ReliefF, SVM-RFE, and random forest, which drastically reduced the dimension of the hyperspectral data set from 215 to 15 features, while improving the accuracy in classifying the salt-treated vegetation pixels from the control pixels by 8.5%. To transform the hyperspectral data set into a multispectral data set, six wavelengths as the center of broad multispectral bands around the most prominent features were determined by a clustering algorithm. The result of salt tolerance assessment of the four wheat lines using the derived multispectral data set was similar to that of the hyperspectral data set. This demonstrates that the proposed feature selection pipeline can be utilized for determining the most informative features and can be a valuable tool in the development of tailored multispectral cameras.
关键词： hyperspectral imaging,Band selection,multispectral imaging,wheat,ensemble feature selection,salt stress,machine learning,plant phenotyping,classification
更新于2025-09-10 09:29:36