- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks
摘要： A high-throughput hyperspectral microscope imaging (HMI) technology with hybrid deep learning (DL) framework defined as “Fusion-Net” was proposed for rapid classification of foodborne bacteria at single-cell level. HMI technology is useful in single-cell characterization, providing spatial, spectral and combined spatial-spectral profiles with high resolution. However, direct analysis of these high-dimensional HMI data is challenging. In this work, HMI data were decomposed into three parts as morphological features, intensity images, and spectral profiles. Multiple advanced DL frameworks including long-short term memory (LSTM) network, deep residual network (ResNet), and one-dimensional convolutional neural network (1D-CNN) were utilized, achieving classification accuracies of 92.2 %, 93.8 %, and 96.2 %, respectively. Taking advantage of fusion strategy, individual DL framework was stacked to form “Fusion-Net” that processed these features simultaneously with improved classification accuracy of up to 98.4 %. Our study demonstrated the ability of DL frameworks to assist HMI technology in single-cell classification as a diagnostic tool for rapid detection of foodborne pathogens.
关键词： Machine learning,Hyperspectral microscopy,Data fusion,Rapid detection,Foodborne pathogen
更新于2025-09-23 15:19:57
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Detection of mites <i>Tyrophagus putrescentiae</i> and <i>Cheyletus eruditus</i> in flour using hyperspectral imaging system coupled with chemometrics
摘要： An automatic method for detecting mites in flour has been established using hyperspectral imaging (HSI) system coupled with chemometrics. Reflectance differences among flour, Tyrophagus putrescentiae, and Cheyletus eruditus were relatively distinct. Majority of the shape features, including area, perimeter, the major, and minor axis in C. eruditus were remarkably larger than flour and T. putrescentiae. Textural features reached the level of the maximum significance except for contrast with one-way analysis of variance. Images under the ant colony optimization (ACO) wavebands in random forests (RF) classification showed at least 89% recognition rates, better than the successive projections algorithm wavebands. Artificial neural networks (ANN) with ACO wavebands gave higher recognition accuracies in training and validation sets than RF. Further analysis verified hyperspectrum with ACO-PCA-ANN (PCA, principal component analysis), which showed over 98% accuracy. This study revealed the promising potential of HSI coupled with ACO-PCA-ANN as an accurate and rapid method for detecting mites in flour.
关键词： hyperspectral imaging,chemometrics,flour,Tyrophagus putrescentiae,Cheyletus eruditus,mites detection
更新于2025-09-23 15:19:57
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Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networksa??
摘要： Foodborne pathogens have become ongoing threats in the food industry, whereas their rapid detection and classification at an early stage are still challenging. To address early and rapid detection, hyperspectral microscope imaging (HMI) technology combined with convolutional neural networks (CNN) was proposed to classify foodborne bacterial species at the cellular level. HMI technology can simultaneously obtain both spatial and spectral information of different live bacterial cells, while two CNN frameworks, U-Net and one-dimensional CNN (1D-CNN), were employed to accelerate the data analysis process. U-Net was used for automating cellular regions of interest (ROI) segmentation, which generated accurate cell-ROI masks in a shorter timeframe than the conventional Otsu or Watershed methods. The 1D-CNN was employed for classifying the spectral profiles extracted from cell-ROI and resulted in a higher accuracy (90%) than k-nearest neighbor (81%) and support vector machine (81%). Overall, the CNN-assisted HMI technology showed potential for foodborne bacteria detection.
关键词： Convolutional neural network,Machine learning,Hyperspectral microscopy,Food safety,Foodborne pathogen,Rapid classification
更新于2025-09-23 15:19:57
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Simple Cosolvent-Treated PEDOT:PSS Films on Hybrid Solar Cells With Improved Efficiency
摘要： This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multi-sensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
关键词： multimodal-,multisource-data fusion,thermal imaging,landcover classification,multiresolution-,Hyperspectral,image analysis and data fusion (IADF)
更新于2025-09-23 15:19:57
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[IEEE 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Shenzhen, China (2019.11.25-2019.11.27)] 2019 16th China International Forum on Solid State Lighting & 2019 International Forum on Wide Bandgap Semiconductors China (SSLChina: IFWS) - Studies on primary lens for LED light source to enhance lateral emission intensity
摘要： Different aspects of the operational constraints of remote sensing inverse problems are thoroughly investigated by simulation studies, using a deterministic method, namely regularized total least squares (RTLS). For demonstration purposes, water vapor profiles retrievals from simulated Suomi NPP Cross-track Infrared Souder (CrIS) hyperspectral measurements are considered. Synthetic CrIS radiances are generated using a line-by-line radiative transfer model (GENSPECT) with ～424 realistic radiosonde profiles and US 1976 standard atmosphere as inputs. These results are also compared with those from a prevalent stochastic method. Our findings show that the stochastic method, even with additional deterministic constraints (truncated singular value decomposition) applied on top of it, is often unable to produce useful retrieval results, i.e., posterior error is more than the a priori error. In contrast, RTLS is able to produce deterministically unique results according to the available information content in the measurements, which could result in a paradigm shift in operational satellite inversion.
关键词： ill-conditioned inverse,Suomi NPP Cross-track Infrared Souder (CrIS),regularized total least squares (RTLS),Hyperspectral infrared sounding
更新于2025-09-23 15:19:57
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Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging
摘要： Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.
关键词： granite soils,artificial neural network,hyperspectral camera,soil water characteristic curve,water content,visible and near-infrared
更新于2025-09-23 15:19:57
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Novel Combined Spectral Indices Derived from Hyperspectral and Laser-Induced Fluorescence LiDAR Spectra for Leaf Nitrogen Contents Estimation of Rice
摘要： Spectra of re?ectance (Sr) and ?uorescence (Sf) are signi?cant for crop monitoring and ecological environment research, and can be used to indicate the leaf nitrogen content (LNC) of crops indirectly. The aim of this work is to use the Sr-Sf features obtained with hyperspectral and laser-induced ?uorescence LiDAR (HSL, LIFL) systems to construct novel combined spectral indices (NCIH-F) for multi-year rice LNC estimation. The NCIH-F is in a form of FWs*Φ + GSIs*Φ, where Φ is the Sr-Sf features, and FWs and GSIs are the feature weights and global sensitive indices for each characteristic band. In this study, the characteristic bands were chosen in di?erent ways. Firstly, the Sr-Sf characteristics which can be the intensity or derivative variables of spectra in 685 and 740 nm, have been assigned as the Φ value in NCIH-F formula. Simultaneously, the photochemical re?ectance index (PRI) formed with 531 and 570 nm was modi?ed based on a variant spectral index, called PRIfraction, with the Sf intensity in 740 nm, and then compared its potential with NCIH-F on LNC estimation. During the above analysis, both NCIH-F and PRIfraction values were utilized to model rice LNC based on the arti?cial neural networks (ANNs) method. Subsequently, four prior bands were selected, respectively, with high FW and GSI values as the ANNs inputs for rice LNC estimation. Results show that FW- and GSI-based NCIH-F are closely related to rice LNC, and the performance of previous spectral indices used for LNC estimation can be greatly improved by multiplying their FWs and GSIs. Thus, it can be included that the FW- and GSI-based NCIH-F constitutes an e?cient and reliable constructed form combining HSL (Sr) and LIFL (Sf) data together for rice LNC estimation.
关键词： hyperspectral LiDAR,combined spectral index,leaf nitrogen content,laser-induced ?uorescence LiDAR
更新于2025-09-23 15:19:57
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Determination of Drying Patterns of Radish Slabs under Different Drying Methods Using Hyperspectral Imaging Coupled with Multivariate Analysis
摘要： Drying kinetics and the moisture distribution map of radish slabs under different drying methods (hot-air drying (HAD), microwave drying (MD), and hot-air and microwave combination drying (HMCD)) were determined and visualized by hyperspectral image (HSI) processing coupled with a partial least square regression (PLSR)-variable importance in projection (VIP) model, respectively. Page model was the most suitable in describing the experimental moisture loss data of radish slabs regardless of the drying method. Dielectric properties (DP, ε) of radish slices decreased with the decrease in moisture content (MC) during MD, and the penetration depth of microwaves in radish was between 0.81 and 1.15 cm. The PLSR-VIP model developed with 38 optimal variables could result in the high prediction accuracies for both the calibration (R2 = 0.967 and RMSEC = 4.32%) and validation (R2 = 0.962 and RMSEC = 4.45%). In visualized drying patterns, the radish slabs dried by HAD had a higher moisture content at the center than at the edges; however, the samples dried by MD contained higher moisture content at the edges. The nearly uniform drying pattern of radish slabs under HMCD was observed in hyperspectral images. Drying uniformity of radish slabs could be improved by the combination drying method, which significantly reduces drying time.
关键词： multivariate analysis,moisture content,drying pattern,hyperspectral imaging,radish
更新于2025-09-23 15:19:57
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Estimation of solar photovoltaic energy curtailment due to volta??watt control
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： nonlocal means,Hyperspectral image,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-23 15:19:57
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Remote sensing bio-control damage on aquatic invasive alien plant species
摘要： Aquatic Invasive Alien Plant (AIAP) species are a major threat to freshwater ecosystems, placing great strain on South Africa’s limited water resources. Bio-control programmes have been initiated in an effort to mitigate the negative environmental impacts associated with their presence in non-native areas. Remote sensing can be used as an effective tool to detect, map and monitor bio-control damage on AIAP species. This paper reconciles previous and current research concerning the application of remote sensing to detect and map bio-control damage on AIAP species. Initially, the spectral characteristics of bio-control damage are described. Thereafter, the potential of remote sensing chlorophyll content and chlorophyll fluorescence as pre-visual indicators of bio-control damage are reviewed and synthesised. The utility of multispectral and hyperspectral sensors for mapping different severities of bio-control damage are also discussed. Popular machine learning algorithms that offer operational potential to classify bio-control damage are proposed. This paper concludes with the challenges of remote sensing bio-control damage as well as proposes recommendations to guide future research to successfully detect and map bio-control damage on AIAP species.
关键词： machine learning algorithms,multispectral sensors,chlorophyll content,Aquatic Invasive Alien Plant (AIAP) species,chlorophyll fluorescence,hyperspectral sensors,Remote sensing,bio-control damage
更新于2025-09-23 15:19:57