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Estimation of pigment concentration in LDPE via in-line hyperspectral imaging and machine learning 通过在线高光谱成像和机器学习估计LDPE中的颜料浓度
Q3 Chemistry Pub Date : 2023-04-03 DOI: 10.1255/jsi.2023.a2
G. Amariei, Anne Schaarup-Kjær, Pernille Klarskov, M. Henriksen, Mogens Hinge
Due to the increasing amount of plastic waste and high-quality demands on recycled plastic interest for in-line composition estimation in plastics has grown the last few years. This study investigates pigment blue 15 : 3 with varying concentrations in LDPE. Samples are investigated with two industrial hyperspectral imaging systems where one has the hyperspectral range from 450 nm to 1050 nm and the other from 950 nm to 1750 nm. A model based on peak ratios of selected bands and model based on a principal component analysis have been tested. The models only predict pigment concentrations between 40.0 wt% and 1.7 × 10–3 wt% if both spectral ranges are combined. Unknown samples containing pigment concentration ranging from 20 wt% to 0.31 wt% were predicted and correlated to the actual pigment concentrations (R2 = :0.977) and the PC-based model outperforms the peak ratio model. The studied approach can be a part of the solution to the plastic challenge and can be transferred to other applications where concentration determination is key.
由于塑料垃圾的数量不断增加,以及对回收塑料的高质量需求,近几年来,人们对塑料在线成分估计的兴趣越来越大。本研究调查了LDPE中不同浓度的颜料蓝15:3。用两个工业高光谱成像系统对样品进行了研究,其中一个高光谱范围为450 nm至1050 nm,另一个高谱范围为950 nm至1750 nm。已经测试了基于所选波段的峰值比率的模型和基于主成分分析的模型。如果两个光谱范围相结合,则模型仅预测40.0wt%和1.7×10-3wt%之间的颜料浓度。预测了含有20 wt%至0.31 wt%颜料浓度的未知样品,并将其与实际颜料浓度相关(R2=:0.977),基于PC的模型优于峰值比模型。所研究的方法可以作为塑料挑战解决方案的一部分,并可以转移到浓度测定是关键的其他应用中。
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引用次数: 1
The hybrid approach—convolutional neural networks and expectation maximisation algorithm—for tomographic reconstruction of hyperspectral images 混合卷积神经网络和期望最大化算法用于高光谱图像的层析重建
Q3 Chemistry Pub Date : 2023-01-31 DOI: 10.1255/jsi.2023.a1
Mads Ahlebæk, Mads Peters, Wei-Chih Huang, Mads Frandsen, René Eriksen, Bjarke Jørgensen
We present a simple, but novel, hybrid approach to hyperspectral data cube reconstruction from computed tomography imaging spectrometry (CTIS) images that sequentially combines neural networks and the iterative expectation maximisation (EM) algorithm. We train and test the ability of the method to reconstruct data cubes of 100 × 100 × 25 and 100 × 100 × 100 voxels, corresponding to 25 and 100 spectral channels, from simulated CTIS images generated by our CTIS simulator. The hybrid approach utilises the inherent strength of the Convolutional Neural Network (CNN) with regards to noise and its ability to yield consistent reconstructions and make use of the EM algorithm’s ability to generalise to spectral images of any object without training. The hybrid approach achieves better performance than both the CNNs and EM alone for seen (included in CNN training) and unseen (excluded from CNN training) cubes for both the 25- and 100-channel cases. For the 25 spectral channels, the improvements from CNN to the hybrid model (CNN + EM) in terms of the mean-squared errors are between 14 % and 26 %. For 100 spectral channels, the improvements between 19 % and 40 % are attained with the largest improvement of 40 % for the unseen data, to which the CNNs are not exposed during the training.
我们提出了一种简单但新颖的混合方法,用于从计算机断层扫描成像光谱(CTIS)图像中重建高光谱数据立方体,该方法依次结合了神经网络和迭代期望最大化(EM)算法。我们训练并测试了该方法从CTIS模拟器生成的模拟CTIS图像中重构对应于25个和100个光谱通道的100 × 100 × 25和100 × 100 × 100体素的数据立方体的能力。混合方法利用卷积神经网络(CNN)在噪声方面的固有强度及其产生一致重建的能力,并利用EM算法无需训练即可推广到任何物体的光谱图像的能力。在25通道和100通道的情况下,对于可见(包括在CNN训练中)和未见(不包括在CNN训练中)数据集,混合方法比单独使用CNN和EM获得了更好的性能。对于25个光谱通道,CNN与混合模型(CNN + EM)在均方误差方面的改进在14%到26%之间。对于100个光谱通道,改进幅度在19%到40%之间,其中对于未见数据的改进幅度最大,达到40%,cnn在训练过程中没有暴露于未见数据。
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引用次数: 1
Comparison of 2D and 3D semantic segmentation in urban areas using fused hyperspectral and lidar data 基于融合高光谱和激光雷达数据的城市区域二维和三维语义分割的比较
Q3 Chemistry Pub Date : 2022-11-07 DOI: 10.1255/jsi.2022.a11
A. Kuras, Anna Jenul, Maximilian Brell, I. Burud
Multisensor data fusion has become a hot topic in the remote sensing research community. This is thanks to significant technological advances and the ability to extract information that would have been challenging with a single sensor. However, sensory enhancement requires advanced analysis that enables deep learning. A framework is designed to effectively fuse hyperspectral and lidar data for semantic segmentation in the urban environment. Our work proposes a method of reducing dimensions by exploring the most representative features from hyperspectral and lidar data and using them for supervised semantic segmentation. In addition, we chose to compare segmentation models based on 2D and 3D convolutional operations with two different model architectures, such as U-Net and ResU-Net. All algorithms have been tested with three loss functions: standard Categorical Cross-Entropy, Focal Loss and a combination of Focal Loss and Jaccard Distance—Focal–Jaccard Loss. Experimental results demonstrated that the 3D segmentation of U-Net and ResU-Net with Focal and Focal–Jaccard Loss functions had significantly improved performance compared to the standard Categorical Cross-Entropy models. The results show a high accuracy score and reflect reality by preserving the complex geometry of the objects.
多传感器数据融合已成为遥感研究界研究的热点。这要归功于重大的技术进步和提取信息的能力,这对于单个传感器来说是一个挑战。然而,感官增强需要高级分析来实现深度学习。设计了一个框架,有效地融合高光谱和激光雷达数据,用于城市环境下的语义分割。我们的工作提出了一种通过探索高光谱和激光雷达数据中最具代表性的特征并将其用于监督语义分割的降维方法。此外,我们选择比较两种不同模型架构(如U-Net和ResU-Net)下基于2D和3D卷积操作的分割模型。所有算法都经过了三种损失函数的测试:标准分类交叉熵,焦点损失和焦点损失和Jaccard距离-焦点- Jaccard损失的组合。实验结果表明,与标准的分类交叉熵模型相比,使用Focal和Focal - jaccard损失函数对U-Net和ResU-Net进行三维分割的性能有显著提高。结果表明,该方法具有较高的精度分数,并通过保留物体的复杂几何形状来反映真实情况。
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引用次数: 1
Comparison of different illumination systems for moisture prediction in cereal bars using hyperspectral imaging technology 利用高光谱成像技术预测谷物棒水分的不同照明系统的比较
Q3 Chemistry Pub Date : 2022-10-25 DOI: 10.1255/jsi.2022.a10
Jaione Echávarri-Dublhán, Miriam Alonso-Santamaría, P. Luri-Esplandiu, María-José Sháiz-Abajo
Moisture content and its distribution is a critical parameter in the production of cereal bars. Inappropriate control of this quality parameter can lead to non-conforming products and excess waste on production lines. In the field of hyperspectral imaging, the search for alternative light sources to stabilised-halogen (cheaper and emitting less heat) is a growing need for the application of this technology in industry. This study compares three different illumination systems for moisture prediction in the visible-near infrared (vis-NIR) range (from 400 nm to 1000 nm). The hyperspectral images were acquired using three illumination systems including two halogen-based systems (stabilised-halogen and conventional-halogen) and an LED-based illumination system. The results showed that halogen-based illumination systems combined with a partial least squares model better predicted moisture in bars. Lower accuracies were obtained when the experiment was performed with an LED-based illumination system, which showed double the error of the halogen-based systems. It was concluded that this is a consequence of the information lost in bands appearing above 850 nm that may be revealing information about the moisture in bars since the second overtone of the water O–H is found at 970 nm. The results demonstrate that conventional halogen-based light systems in the vis-NIR range are a promising method for moisture prediction in cereal bars.
水分含量及其分布是谷物棒生产中的一个重要参数。对这一质量参数控制不当会导致不合格产品和生产线上的多余浪费。在高光谱成像领域,寻找稳定卤素的替代光源(更便宜,散发更少的热量)是该技术在工业中的应用日益增长的需求。本研究比较了三种不同的照明系统在可见光-近红外(vis-NIR)范围内(从400纳米到1000纳米)的水分预测。使用三种照明系统获得高光谱图像,包括两种基于卤素的系统(稳定卤素和常规卤素)和一种基于led的照明系统。结果表明,卤素照明系统结合偏最小二乘模型能更好地预测酒吧内的湿度。当采用led照明系统进行实验时,实验精度较低,其误差是卤素照明系统的两倍。结论是,这是出现在850 nm以上波段的信息丢失的结果,该波段可能揭示了bar中水分的信息,因为在970 nm处发现了水O-H的第二个泛音。结果表明,在可见光-近红外范围内,传统的卤素光系统是一种很有前途的谷物棒水分预测方法。
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引用次数: 1
Reflectance spectra and AVIRIS-NG airborne hyperspectral data analysis for mapping ultramafic rocks in igneous terrain 反射光谱和AVIRIS-NG航空高光谱数据分析用于绘制火成岩地形中的超镁铁质岩石
Q3 Chemistry Pub Date : 2022-10-19 DOI: 10.1255/jsi.2022.a9
K. Tamilarasan, S. Anbazhagan, S. Maheswaran, S. Ranjithkumar, K. Kusuma, V. Rajesh
The layered Sittampundi Anorthosite Complex is covered by mafic and ultramafic rocks including anorthosite, gabbro, pyroxenite and other igneous rocks. The ultramafic terrain has frequently undergone metamorphism. In the present study, laboratory spectral measurements were carried out from mafic, ultramafic and felsic rocks in the 350–2500 nm spectral range to characterise their diagnostic spectral features and for further utilisation for rock-type mapping. In 2016, the Sittampundi complex was covered by an AVIRIS-NG airborne survey jointly conducted by the Space Application Centre (SAC-ISRO) and Jet Propulsion Laboratory (NASA). The level-2 AVIRIS-NG data was obtained from SAC and used to interpret various rock types. ENVI 5.3 software was used for digital image processing of the AVIRIS-NG airborne hyperspectral data. The continuum-removed spectra of major rock types including anorthosite, meta-anorthosite, gabbro, meta-gabbro, pyroxenite, pegmatite, granite, gneiss and migmatite were critically analysed and their diagnostic absorption features correlated with chemistry and mineralogy. The AVIRIS-NG data analyses include bad band removal, minimum noise fraction transformation (MNF) and band combination. Out of various band combinations, the MNF composite images B456, B546 and B561 provided an enhanced output for the delineation of various rock types in the ultramafic terrain.
层状Sitampundi Anorthosite杂岩被镁铁质和超镁铁质岩石覆盖,包括斜长岩、辉长岩、辉石岩和其他火成岩。超镁铁质地形经常发生变质作用。在本研究中,对350–2500 nm光谱范围内的镁铁质、超镁铁质和长英质岩石进行了实验室光谱测量,以表征其诊断光谱特征,并进一步用于岩石类型测绘。2016年,空间应用中心(SAC-ISRO)和喷气推进实验室(NASA)联合进行的AVIRIS-NG机载调查覆盖了Sitampundi综合体。2级AVIRIS-NG数据来自SAC,用于解释各种岩石类型。ENVI 5.3软件用于AVIRIS-NG机载高光谱数据的数字图像处理。对主要岩石类型(包括斜长岩、变斜长岩、辉长岩、变辉长岩、辉石岩、伟晶岩、花岗岩、片麻岩和混合岩)的连续谱进行了严格分析,并将其诊断吸收特征与化学和矿物学相关联。AVIRIS-NG数据分析包括坏频带去除、最小噪声分数变换(MNF)和频带组合。在各种波段组合中,MNF合成图像B456、B546和B561为超镁铁质地形中各种岩石类型的描绘提供了增强的输出。
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引用次数: 2
Potential for spectral imaging applications on the small farm: a review 光谱成像在小农场应用的潜力:综述
Q3 Chemistry Pub Date : 2022-10-14 DOI: 10.1255/jsi.2022.a8
M. Eady
Advancements in optics and miniaturisation have resulted in multi- and hyperspectral imaging systems becoming more approachable in terms of cost, practicality and useability. Globally, the majority of farms are considered to be small farms (<2 hectares). Many spectral imaging applications have been associated with agricultural commodities over the years. However, due to the cost, technology hurdles and complex statistical modelling methods, these applications have mainly been implemented in larger monoculture settings where the method development time required can be met with and substantiated through higher profits gained and reduced labour in the long term. Recent years have seen advancements in spectral imaging technologies as well as open-source systems that have the potential for application on smaller, more diversified farms. There are many hurdles to face before spectral imaging technologies see widespread application on smaller farms, but technologies are advancing rapidly. Here, the current state of spectral imaging in small farm applications is evaluated, along with the potential for low-cost and open-source spectral imaging systems. Emphasis is placed on challenges which require addressing prior to approachable spectral imaging for the small farm.
光学和小型化的进步使多光谱和高光谱成像系统在成本、实用性和可使用性方面变得更加接近。在全球范围内,大多数农场被认为是小型农场(<2公顷)。多年来,许多光谱成像应用都与农产品有关。然而,由于成本、技术障碍和复杂的统计建模方法,这些应用主要在较大的单一栽培环境中实施,在这些环境中,可以通过获得更高的利润和减少长期劳动力来满足和证实所需的方法开发时间。近年来,光谱成像技术以及开源系统取得了进步,这些技术有可能应用于规模更小、更多样化的农场。在光谱成像技术在小型农场广泛应用之前,还有许多障碍需要面对,但技术正在迅速发展。在这里,评估了小农场应用中光谱成像的现状,以及低成本和开源光谱成像系统的潜力。重点是在小型农场可接近的光谱成像之前需要解决的挑战。
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引用次数: 1
A comparison of common factor-based methods for hyperspectral image exploration: principal components analysis, maximum autocorrelation factors (MAF), minimum noise factors (MNF) and maximum difference factors 基于常用因子的高光谱图像探测方法的比较:主成分分析、最大自相关因子(MAF)、最小噪声因子(MNF)和最大差异因子
Q3 Chemistry Pub Date : 2022-08-16 DOI: 10.1255/jsi.2022.a6
Neal Gallagher
Principal components analysis (PCA), maximum autocorrelation factors (MAF), minimum noise factors (MNF) and maximum difference factors (MDF) models are common factor-based models used for analysis of hyperspectral images. The models can be posed as maximisation problems that result in a symmetric eigenvalue problem (SEP) for each model. The SEPs allow a simple theoretical comparison of the models using a PCA metaphor with MAF, MNF and MDF describable as weighted PCA models. The examples show that the different methods captured different signals in the images that can be examined individually or combined synergistically allowing for additional modelling and extended visualisation. MDF is a factor-based edge detection model that not only allows for additional visualisation but the opportunity to identify and exclude (or highlight) edge signal in the images. An example shows that models can also be used synergistically for finding and elucidating anomalies. In the example, MDF showed the highest sensitivity of the models studied for anomaly detection.
主成分分析(PCA)、最大自相关因子(MAF)、最小噪声因子(MNF)和最大差分因子(MDF)模型是高光谱图像分析中常用的基于因子的模型。模型可以被提出为最大化问题,导致每个模型的对称特征值问题(SEP)。sep允许使用PCA比喻与MAF, MNF和MDF描述为加权PCA模型的模型进行简单的理论比较。这些例子表明,不同的方法在图像中捕获了不同的信号,这些信号可以单独检查,也可以协同组合,从而实现额外的建模和扩展的可视化。MDF是一种基于因素的边缘检测模型,它不仅允许额外的可视化,而且有机会识别和排除(或突出显示)图像中的边缘信号。实例表明,这些模型也可以协同用于发现和解释异常。在本例中,MDF显示了所研究模型中异常检测的最高灵敏度。
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引用次数: 0
Impact of water vapour on polymer classification using in situ short-wave infrared hyperspectral imaging 水蒸气对原位短波红外高光谱成像聚合物分类的影响
Q3 Chemistry Pub Date : 2022-06-01 DOI: 10.1255/jsi.2022.a5
Muhammad Shaikh, Benny Thörnberg
Hyperspectral remote sensing is known to suffer from wavelength bands blocked by atmospheric gases. Short-wave infrared hyperspectral imaging at in situ installations is shown to be affected by water vapour even if the pathlength of light through air is only hundreds of centimetres. This impact is especially noticeable with large variations of relative humidity, the coefficient of variation reaching 5 % in our test case. Using repeated calibrations of imaging system at the same relative humidity as in the measurement, we were able to reduce the coefficient of variation to 1 %. The measurement variations are also shown to induce significant error in material classification. Polymer type identification was selected as the test case for material classification. The measurement variations due to the change in relative humidity are shown to result in 20 % classification error at its minimum. With repeated calibrations or by eliminating themost affected wavelength bands from measurements, we were able to reduce the classification error to less than 1 %.Such improvement of measurement and classification precision may be important for industrial applications such as wastesorting, polymer classification etc.
众所周知,高光谱遥感会受到被大气气体阻挡的波段的影响。即使光通过空气的路径长度只有几百厘米,就地装置的短波红外高光谱成像也会受到水蒸气的影响。这种影响在相对湿度变化较大时尤为明显,在我们的测试案例中,变化系数达到5%。在与测量时相同的相对湿度下对成像系统进行重复校准,我们能够将变异系数降低到1%。测量变化也会导致材料分类的显著误差。选择聚合物类型识别作为材料分类的测试案例。由于相对湿度的变化而引起的测量变化表明,其最小分类误差为20%。通过反复校准或从测量中消除最受影响的波长波段,我们能够将分类误差降低到1%以下。这种测量和分类精度的提高对废物分类、聚合物分类等工业应用具有重要意义。
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引用次数: 0
Hyperspectral image non-linear unmixing using joint extrinsic and intrinsic priors with L1/2-norms to non-negative matrix factorisation 基于l1 /2范数的高光谱图像非线性解混非负矩阵分解
Q3 Chemistry Pub Date : 2022-04-07 DOI: 10.1255/jsi.2022.a4
K. Priya, K. Rajkumar
Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance. HU enhances the quality of both spectral and spatial dimensions of the image by modifying the endmember and abundance parameters of the hyperspectral images. There are several HU algorithms available in the literature based on the linear mixing model (LMM) that deals with the microscopic contents of the pixels in the images. Non-negative matrix factorisation (NMF) is the prominent method widely used in LMMs that simultaneously estimates both the endmembers and abundances parameters along with some residual factors of the image to improve the quality of unmixing. In addition to this, the quality of the image is enhanced by incorporating some constraints to both endmember and abundance matrices with the NMF method. However, all the existing methods apply any of these constraints to the endmember and abundance matrices by considering the linearity features of the images. In this paper, we propose an unmixing model called joint extrinsic and intrinsic priors with L1/2 norms to non-negative matrix factorisation (JEIp L1/2-NMF) that applies multiple constraints simultaneously to both endmember and abundance matrices of the hyperspectral image to enhance its quality. Three main external and internal constraints such as minimum volume, sparsity and total variation are applied to both the endmembers and abundance parameters of the image. In addition, a L1/2-norms is imposed to extract good quality spectral data. Therefore, the proposed method enhances spatial as well as spectral data and considers the non-linearity of the pixels in the image by adding a residual term to the model. Performance of our proposed model is measured by using different quality measuring indexes on four benchmark public datasets and found that the proposed method shows outstanding performance compared to all the conventional baseline methods. Further, we also evaluated the performance of our method by varying the number of endmembers empirically and concluded that less than five endmembers provides high-quality spectral and spatial data during the unmixing process.
高光谱解混(HU)是图像处理中最活跃的新兴领域之一,用于估计高光谱图像的末端成员和丰度。HU通过修改高光谱图像的端元和丰度参数来提高图像的光谱和空间维度的质量。文献中有几种基于线性混合模型(LMM)的HU算法,该模型处理图像中像素的微观内容。非负矩阵分解(NMF)是LMM中广泛使用的突出方法,它同时估计端元和丰度参数以及图像的一些残差因子,以提高解混质量。除此之外,通过使用NMF方法对端元矩阵和丰度矩阵引入一些约束,提高了图像的质量。然而,所有现有的方法都通过考虑图像的线性特征来将这些约束中的任何一个应用于端元和丰度矩阵。在本文中,我们提出了一种称为具有非负矩阵因子分解的L1/2范数的联合外在和内在先验的解混模型(JEIp L1/2-NMF),该模型同时对高光谱图像的端元和丰度矩阵应用多个约束,以提高其质量。三个主要的外部和内部约束,如最小体积、稀疏性和总变化,被应用于图像的端元和丰度参数。此外,施加L1/2范数以提取高质量的光谱数据。因此,所提出的方法增强了空间和光谱数据,并通过向模型中添加残差项来考虑图像中像素的非线性。通过在四个基准公共数据集上使用不同的质量测量指标来测量我们提出的模型的性能,发现与所有传统的基线方法相比,该方法表现出出色的性能。此外,我们还通过根据经验改变端元的数量来评估我们的方法的性能,并得出结论,在解混过程中,不到五个端元可以提供高质量的光谱和空间数据。
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引用次数: 0
Data processing of three-dimensional vibrational spectroscopic chemical images for pharmaceutical applications 制药用三维振动光谱化学图像的数据处理
Q3 Chemistry Pub Date : 2022-03-30 DOI: 10.1255/jsi.2022.a3
Hannah Carruthers, D. Clark, F. Clarke, K. Faulds, D. Graham
Vibrational spectroscopic chemical imaging is a powerful tool in the pharmaceutical industry to assess the spatial distribution of components within pharmaceutical samples. Recently, the combination of vibrational spectroscopic chemical mapping with serial sectioning has provided a means to visualise the three-dimensional (3D) structure of a tablet matrix. There are recognised knowledge gaps in current tablet manufacturing processes, particularly regarding the size, shape and distribution of components within the final drug product. The performance of pharmaceutical tablets is known to be primarily influenced by the physical and chemical properties of the formulation. Here, we describe the data processing methods required to extract quantitative domain size and spatial distribution statistics from 3D vibrational spectroscopic chemical images. This provides a means to quantitatively describe the microstructure of a tablet matrix and is a powerful tool to overcome knowledge gaps in current tablet manufacturing processes, optimising formulation development.
振动光谱化学成像是制药行业评估药物样品中成分空间分布的有力工具。最近,振动光谱化学图谱与连续切片的结合提供了一种可视化片剂基质三维(3D)结构的方法。目前的片剂生产过程中存在公认的知识差距,特别是在最终药品中成分的大小、形状和分布方面。已知片剂的性能主要受制剂的物理和化学性质的影响。在这里,我们描述了从3D振动光谱化学图像中提取定量域大小和空间分布统计信息所需的数据处理方法。这为定量描述片剂基质的微观结构提供了一种手段,也是克服当前片剂生产过程中知识空白、优化配方开发的有力工具。
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引用次数: 0
期刊
Journal of Spectral Imaging
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