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- 2018
- 2015
- classification
- Fruit defects
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- Hyperspectral imaging
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- plant cell diseases
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- Applied Physics
- Measurement and Control Technology and Instruments
- Brno University of Technology
- University of Sciences, Technique and Technology Bamako
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
- Institut National Polytechnique Felix Houphou?t-Boigny Yamoussoukro
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Separable-spectral convolution and inception network for hyperspectral image super-resolution
摘要： Due to the limitation of the imaging system, it is hard to get Hyperspectral Image (HSI) with very high spatial resolution. Super-Resolution (SR) is a handling missing data technology to restore high-frequency information from the low-resolution image, can be used to solve this problem. Recently, Deep Learning (DL) has achieved great performance in computer vision, including SR. However, most DL-based HSI SR methods neglect the spectral disorder caused by normal 2D convolution. This paper proposes a novel end–end deep learning-based network named Separable-Spectral and Inception Network (SSIN) for HSI SR. In SSIN, the feature extraction module independently extracts features of each band image, and then these features are fused together to further exploit residual image by using feature fusion module. In reconstruction module, a multi-path connection is built to obtain features of different levels to restore high spatial resolution image in a coarse-to-fine manner. Experiments are implemented on two datasets include both indoor and airborne HSIs, and the performances of SSIN are evaluated in different conditions. Experimental results show that adding several separable spectral convolutions and multi-path connection in a deep network can greatly improve the SR performance, and SSIN achieves higher accuracy and better visualization compare with other methods.
关键词： Hyperspectral Image,Separable-spectral convolution,Deep learning,Super-resolution,Multi-path reconstruction
更新于2025-09-23 15:22:29
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Extracting graphite sketch of the mural using Hyper-Spectral Imaging method
摘要： Many contaminants appear in murals as time passes, which make the original mural blurred and difficult to recognize; therefore, extracting a clear graphite sketch of the mural is significant. In this study, we used invisible spectra, particularly near-infrared (NIR) bands, to detect the graphite information and strengthen the features of the mural information to obtain a graphite sketch. This is the first study to extract the contour line from the draft of the mural using hyper-spectral imaging (HSI) technology. First, spectral matching methods were used to identify the pigment of the contour line and graphite was determined as the main pigment of the draft. Then, the characteristic bands were selected by analysing the spectra of the pigments. After that, the information extraction method was used to extract the graphite information. The results showed that the method could improve the efficiency of graphite information extraction significantly. The key steps of the current method involved extracting the graphite contour line end-member spectrum, followed by mapping the grey image of the graphite contour line spectrum. Finally, the visually enhanced image was reconstructed using the alpha blending fusion method with the original visible image and the graphite information image. The efficiency of results is evaluated by quantitative methods. The study also explained and discussed the two key points of election thresholds in obtaining the graphite sketch. These results demonstrate that the method is efficient for extracting graphite sketch based on hyper-spectral data of mural, and that it could provide useful information to explore cultural relics and to support some other protection researches.
关键词： Hyper-Spectral Imaging,information extraction,visual enhancement,graphite contour line,murals
更新于2025-09-23 15:22:29
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Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection
摘要： In this article, we describe a novel unsupervised spectral image segmentation algorithm. This algorithm extends the classical Gaussian Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position. Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the number of classes. We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation. Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.
关键词： Spectral images,Gaussian Mixture Model,Model selection,Spatial information,Unsupervised segmentation
更新于2025-09-23 15:22:29
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Metabolic profiling of early lactation dairy cows using milk mid-infrared spectra
摘要： Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of mid-infrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of south-eastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of β-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R2) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R2 ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.
关键词： mid-infrared spectral prediction,metabolic profile,energy balance,ketosis
更新于2025-09-23 15:22:29
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Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms
摘要： The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.
关键词： spectral tracking,time series,plant phenotyping,hyperspectral imaging,disease detection
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Blind Nonlinear Hyperspectral Unmixing Using an <tex>$\ell_{q}$</tex> Regularizer
摘要： Hyperspectral unmixing consists of estimating pure material spectra (endmembers) and their corresponding abundances in hyperspectral images. In this paper, a blind nonlinear hyperspectral unmixing algorithm is presented. The algorithm promotes sparse abundance maps using an lq regularizer and assumes that the spectra are mixed according to an extension to generalized bilinear model, called the Fan model. The algorithm is evaluated using both simulated and real hyperspectral data.
关键词： non-negative matrix factorization,Spectral unmixing,bilinear model,hyperspectral images
更新于2025-09-23 15:22:29
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Identification of Wheat Yellow Rust Using Optimal Three-Band Spectral Indices in Different Growth Stages
摘要： Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.
关键词： yellow rust disease,three-band spectral index,different growth stages,hyperspectral remote sensing,wheat infection
更新于2025-09-23 15:22:29
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Hyperspectral Face Recognition with Patch-Based Low Rank Tensor Decomposition and PFFT Algorithm
摘要： Hyperspectral imaging technology with sufficiently discriminative spectral and spatial information brings new opportunities for robust facial image recognition. However, hyperspectral imaging poses several challenges including a low signal-to-noise ratio (SNR), intra-person misalignment of wavelength bands, and a high data dimensionality. Many studies have proven that both global and local facial features play an important role in face recognition. This research proposed a novel local features extraction algorithm for hyperspectral facial images using local patch based low-rank tensor decomposition that also preserves the neighborhood relationship and spectral dimension information. Additionally, global contour features were extracted using the polar discrete fast Fourier transform (PFFT) algorithm, which addresses many challenges relevant to human face recognition such as illumination, expression, asymmetrical (orientation), and aging changes. Furthermore, an ensemble classifier was developed by combining the obtained local and global features. The proposed method was evaluated by using the Poly-U Database and was compared with other existing hyperspectral face recognition algorithms. The illustrative numerical results demonstrate that the proposed algorithm is competitive with the best CRC_RLS and PLS methods.
关键词： spectral and spatial information,polar discrete fast Fourier transform,band fusion,ensemble classifier,global and local features,tensor decomposition,hyperspectral images
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Carbon Dioxide and Water Vapour Fluxes of a Alkaline Fen and Their Dependence on Reflectance
摘要： This study shows results of parallel measurements of greenhouse gases fluxes (carbon dioxide and water vapour) and canopy reflectance of alkaline fen. Fluxes were measured using eddy-covariance technique on micrometeorological station located in the Upper Biebrza Basin (NE Poland) in Rogo?ynek Village. Study site is located in the Biebrza National Park which was established to protect one of the biggest coherent lowland wetland area in the Central Europe. Statistical relations of reflectance and spectral indices with fluxes were calculated based on measurements during two growing season (2015 and 2016). Four types of functions were examined: linear, quadratic, exponential and logarithmic and for two timestamps: half-hour and day. The correlation between carbon dioxide fluxes and reflectance is better than for water vapour fluxes. For both carbon dioxide and water vapour we obtained higher correlations coefficients using selected spectral indices than using reflectance and higher correlations for daily timestamp than for half-hour fluxes.
关键词： greenhouse gases,eddy-covariance technique,spectral indices,Biebrza River valley,wetland
更新于2025-09-23 15:22:29
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Evaluation of Signal Regeneration Impact on the Power Efficiency of Long-Haul DWDM Systems
摘要： Due to potential economic benefits and expected environmental impact, the power consumption issue in wired networks has become a major challenge. Furthermore, continuously increasing global Internet traffic demands high spectral efficiency values. As a result, the relationship between spectral efficiency and energy consumption of telecommunication networks has become a popular topic of academic research over the past years, where a critical parameter is power efficiency. The present research contains calculation results that can be used by optical network designers and operators as guidance for developing more power efficient communication networks if the planned system falls within the scope of this paper. The research results are presented as average aggregated traffic curves that provide more flexible data for the systems with different spectrum availability. Further investigations could be needed in order to evaluate the parameters under consideration taking into account particular spectral parameters, e.g., the entire C-band.
关键词： DWDM,phase shift keying,differential phase shift keying,power consumption,spectral efficiency,sub-band spacing,WDM networks,single-line rate,optical fibre networks,power efficiency,energy efficiency
更新于2025-09-23 15:22:29