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- 实验方案
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过滤筛选
- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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A Spectral Assignment-Oriented Approach to Improve Interpretability and Accuracy of Proxy Spectral-Based Models
摘要： In modeling chemical attributes using hyperspectral data, nonlinear relationships between the predictor and the response are frequent. The common nonlinear modeling techniques improve prediction accuracy but suffer from low interpretability of the models. In this paper, we demonstrate a new multivariate modeling method, denoted as spectral assignment-oriented partial least squares (SAO-PLS), which is designed to provide a nonlinear modeling solution with strong interpretability products. The need for this approach is apparent when a given sample population consists of different spectral features for different levels of the response. Accordingly, the suggested SAO-PLS algorithm segments the data in an optimal location on the response distribution by maximizing the difference in spectral assignments between two clusters. SAO-PLS is applied here to two test cases with different characteristics: 1) an established data set containing airborne hyperspectral data of asphaltic roads, merged with in situ measured dynamic friction values captured using a standardized method and 2) a soil spectral library, spectrally measured with an analytical spectral device spectrometer, to which organic carbon measurements were applied. Our results demonstrate the superiority of SAO-PLS over partial least-squares regression for both model accuracy and interpretability, providing a deeper understanding of the underlying processes.
关键词： proxy models,Chemometrics,remote sensing,hyperspectral data,nonlinear modeling
更新于2025-09-04 15:30:14
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Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland
摘要： Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake ef?ciency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our ?ndings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the ?rst high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m?2 s?1 and 12 μmol m?2 s?1 were predominant (86.3% of the total area). For hollows, our results revealed, for the ?rst time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake ef?ciency for the whole bog.
关键词： Shortwave Airborne Spectrographic Imager (SASI),Compact Airborne Spectrographic Imager (CASI),carbon uptake,gravimetric water content,normalized difference water index (NDWI),photosynthesis,airborne hyperspectral,bog,Mer Bleue,peatlands
更新于2025-09-04 15:30:14
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A Novel Near-infrared Hyperspectral Absorption/Scattering Imaging Method using Multiple Ground Plates for Evaluating Polymer Composites
摘要： This paper proposes a nondestructive method of evaluating polymer composites using near-infrared (NIR) diffuse reflection spectroscopy with multiple ground plates. Wavelength-dependent absorption and reduced scattering coefficients were acquired to evaluate the chemical structure and the concentration of the substances from absorption and to determine the size and the dispersity of filler in the polymer domain from scattering. NIR spectra of the sample were measured on multiple ground plates, namely, “ground-plate-dependent” diffuse reflection spectra. The effects of the external reflection on the ground-plate-dependent diffuse reflection spectra were subsequently removed. The internal reflection coefficient was calculated based on the difference between the diffuse reflectances of the neat resin and ground plates without prior information of the incident angle of light and the refractive index of sample. The external reflection coefficient was evaluated by the gap of diffuse reflectances between the sample and a white ground plate. After the corrections of reflections, the spectra were fitted by a physical model of light propagation based on the two-flex theory to acquire the absorption and the reduced scattering coefficients. The calculated absorption coefficients indicated a good linear relationship with particle concentration. The calculated reduced scattering coefficients agreed with the theoretical values by Mie scattering theory. It was demonstrated that the proposed method achieved to the simultaneous evaluation of particulate-filler concentrations and sizes in polymer composites.
关键词： Near-infrared,Hyperspectral imaging,Absorption,Scattering,Polymer composites
更新于2025-09-04 15:30:14
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Spatial Functional Data Analysis for the Spatial-Spectral Classification of Hyperspectral Imagery
摘要： Although support vector classi?ers for hyperspectral imagery traditionally exploit spectral information alone, there has been increasing interest in spatial–spectral classi?ers that incorporate spatial context due to the potential for signi?cant performance improvement over spectral-only approaches. Accordingly, a new approach for spatial–spectral classi?cation is introduced which incorporates spatial information into a prior hyperspectral classi?er driven by functional data analysis (FDA) applied to continuous spectral functions. FDA permits functional properties—such as the smoothness inherent to spectral signatures—to inform hyperspectral classi?cation. The proposed spatial FDA (SFDA) incorporates an additional spatial coherency factor that attempts to ensure that each pixel is represented with a spectral curve that is similar to those of its nearest spatial neighbors. Experimental results demonstrate that the proposed SFDA coupled with a support vector classi?er yields results superior to other state-of-the-art spatial–spectral techniques for hyperspectral classi?cation.
关键词： Feature extraction,functional data analysis (FDA),hyperspectral classi?cation
更新于2025-09-04 15:30:14
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[American Society of Agricultural and Biological Engineers 2017 Spokane, Washington July 16 - July 19, 2017 - ()] 2017 Spokane, Washington July 16 - July 19, 2017 - <i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>
摘要： Variety classification of maize kernels was evaluated using near infrared (NIR) hyperspectral imaging in this work. Firstly, NIR hyperspectral images of kernels of four widely used maize varieties were acquired within effective spectral range of 1000-2500 nm. Spectral math was used to compensate for minor lighting differences, and band math combined with threshold method was used to remove the background from images. Minimum noise fraction (MNF) was adopted to reduce noise. Texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) as appearance character of each maize kernel were calculated and extracted to establish classification model combined with spectra data. Moving average smoothing and standard normal variate were applied on the raw spectra extracted from hyperspectral images. Four optimal wavelengths (1352.20 nm, 1615.50 nm, 1733.10 nm, and 2478.20 nm) were selected by competitive adaptive reweighted sampling (CARS) method. Partial least squares discriminant analysis (PLSDA) was employed to build varieties classification models, based on full wavelength data, the four wavelengths data, and combination of spectral and textural features at four wavelengths, respectively. Results demonstrated that PLSDA model based on combination of spectral and textural features had the best performance with accuracies of 0.89, 0.83 for calibration and prediction set, which indicated the hyperspectral imaging technique with combination of spectral and textural features had a potential of application for variety classification.
关键词： Variety classification,Maize kernel,NIR hyperspectral imaging,Partial least squares discriminant analysis (PLSDA),Competitive adaptive reweighted sampling (CARS) method
更新于2025-09-04 15:30:14
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Data Fusion of Two Hyperspectral Imaging Systems with Complementary Spectral Sensing Ranges for Blueberry Bruising Detection
摘要： Currently, the detection of blueberry internal bruising focuses mostly on single hyperspectral imaging (HSI) systems. Attempts to fuse different HSI systems with complementary spectral ranges are still lacking. A push broom based HSI system and a liquid crystal tunable filter (LCTF) based HSI system with different sensing ranges and detectors were investigated to jointly detect blueberry internal bruising in the lab. The mean reflectance spectrum of each berry sample was extracted from the data obtained by two HSI systems respectively. The spectral data from the two spectroscopic techniques were analyzed separately using feature selection method, partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM), and then fused with three data fusion strategies at the data level, feature level, and decision level. The three data fusion strategies achieved better classification results than using each HSI system alone. The decision level fusion integrating classification results from the two instruments with selected relevant features achieved more promising results, suggesting that the two HSI systems with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve blueberry internal bruising detection. This study was the first step in demonstrating the feasibility of the fusion of two HSI systems with complementary spectral ranges for detecting blueberry bruising, which could lead to a multispectral imaging system with a few selected wavelengths and an appropriate detector for bruising detection on the packing line.
关键词： data fusion,blueberry,hyperspectral imaging,bruising
更新于2025-09-04 15:30:14