Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm⁻².     

Applications of Vibrational Spectroscopy for Analysis of Connective Tissues

Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how “spectral fingerprints” can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.     

Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology

A porcelain insulator is an important part to ensure that the insulation requirements of power equipment can be met. Under the influence of their structure, porcelain insulators are prone to mechanical damage and cracks, which will reduce their insulation performance. After a long-term operation, crack expansion will eventually lead to breakdown and safety hazards. Therefore, it is of great significance to detect insulator cracks to ensure the safe and reliable operation of a power grid. However, most traditional methods of insulator crack detection involve offline detection or contact measurement, which is not conducive to the online monitoring of equipment. Hyperspectral imaging technology is a noncontact detection technology containing three-dimensional (3D) spatial spectral information, whereby the data provide more information and the measuring method has a higher safety than electric detection methods. Therefore, a model of positioning and state classification of porcelain insulators based on hyperspectral technology is proposed. In this model, image data were used to extract edges to locate cracks, and spectral information was used to classify the surface states of porcelain insulators with EfficientNet. Lastly, crack extraction was realized, and the recognition accuracy of cracks and normal states was 96.9%. Through an analysis of the results, it is proven that the crack detection method of a porcelain insulator based on hyperspectral technology is an effective non-contact online monitoring approach, which has broad application prospects in the era of the Internet of Things with the rapid development of electric power.     

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Near-infrared (NIR) hyperspectral imaging system was used to detect five concentration levels of ochratoxin A (OTA) in contaminated wheat kernels. The wheat kernels artificially inoculated with two different OTA producing Penicillium verrucosum strains, two different non-toxigenic P. verrucosum strains, and sterile control wheat kernels were subjected to NIR hyperspectral imaging. The acquired three-dimensional data were reshaped into readable two-dimensional data. Principal Component Analysis (PCA) was applied to the two dimensional data to identify the key wavelengths which had greater significance in detecting OTA contamination in wheat. Statistical and histogram features extracted at the key wavelengths were used in the linear, quadratic and Mahalanobis statistical discriminant models to differentiate between sterile control, five concentration levels of OTA contamination in wheat kernels, and five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels. The classification models differentiated sterile control samples from OTA contaminated wheat kernels and non-OTA producing P. verrucosum inoculated wheat kernels with a 100% accuracy. The classification models also differentiated between five concentration levels of OTA contaminated wheat kernels and between five infection levels of non-OTA producing P. verrucosum inoculated wheat kernels with a correct classification of more than 98%. The non-OTA producing P. verrucosum inoculated wheat kernels and OTA contaminated wheat kernels subjected to hyperspectral imaging provided different spectral patterns.     

Effective detection of benzoyl peroxide in flour based on parameter selection of Raman hyperspectral system

Penetration depth and spatial resolution of Raman hyperspectral imaging system were studied for effective detection of benzoyl peroxide in flour. The determinations of parameters were achieved by using the single-band background-correct image of a benzoyl peroxide Raman characteristic band and a simple threshold method. The selected parameters were used to detect mixture samples with different concentrations. Percentage of detected benzoyl peroxide pixels was positively correlated to its concentration. The result shows that parameters selected in this study are effective for the detection of benzoyl peroxide additive in flour and can be used for quantitative analysis in the future.     

基于独立成分分析的高光谱图像异常检测

针对高光谱图像中背景及目标先验知识未知条件下的异常目标检测问题,提出了一种基于独立成分分析(ICA)的异常探测算法.首先估计原始数据的虚拟维(VD)以确定要分离的独立成分个数,在此基础上进行快速独立成分分析(FastICA),然后基于平均局部奇异度选择含异常信息较多的独立成分,最后使用丰度量化算法得到异常目标的丰度图像...     

一种改进的无链表3DSPITH算法用于高光谱图像压缩

 针对大孔径静态干涉成像光谱仪(LASIS)的成像特点,提出了一种基于三维非对称等长树小波变换的无链表SPITH算法结合ROI的图像压缩方案。首先,对干涉高光谱图像进行三维非对称等长树离散小波变换。其次,采用ROI方法对主要的光谱系数进行保护。最后,采用改进的三维无链表SPITH算法,编码干涉高光谱图像的小波变换域。实验结果表明,该方法在8∶1压缩比下,获得大于40 dB的平均峰值信噪比,同时有效地保护了光谱信息。     

基于HSI高光谱和TM图像的土地盐渍化信息提取方法

选择黄河三角洲垦利县代表性盐碱化区域为研究区,以2011年3月15日HJ-1A卫星HSI高光谱影像和2011年3月22日TM影像为信息源,经几何纠正、图像裁剪、大气校正等预处理,分析不同盐渍化程度土地、水体、滩涂等主要地类的光谱特征,确定地类信息提取特征波段。结合土壤盐分含量,采用定量与定性相结合规则,构建地类信息提取模型,以决策树分类方法进行图像分类,提取土地盐渍化信息。利用地表点位土壤含盐量数据对地表土地盐渍化程度的化学分析结果,对遥感解译数据进行精度验证,并对高光谱和多光谱影像的分类精度进行比较分析。结果表明：HSI图像的总体分类精度达96.43%,Kappa系数为95.59%,而TM图像的总体分类精度为89.17%,Kappa系数为86.74%,说明相比多光谱TM数据,基于高光谱图像可以更为准确有效地提取土地盐渍化信息;由分类结果图可以看出,高光谱影像土地盐渍化的区分度高于多光谱影像。该研究探索了高光谱图像土地盐渍化信息的提取技术方法,提供了不同盐渍化土地的分布比例数据,可为黄河三角洲滨海盐碱土地资源的科学利用与管理提供决策依据。     

近红外高光谱图像结合CARS算法对鸭梨SSC含量定量测定

高光谱数据量大、 维数高且原始光谱噪声明显、 散射严重等特征导致光谱建模时关键波长变量提取困难。 基于此,提出采用竞争性自适应重加权算法(CARS)对近红外高光谱数据进行关键变量选择。 鸭梨作为研究对象。 采用决定系数r2、 预测均方根误差RMSEP和验证集标准偏差和预测集标准偏差的比值RPD值进行模型性能评估。 基于选择的关键变量建立PLS模型(CARS-PLS)与全光谱变量建立的PLS模型进行比较发现CARS-PLS模型仅仅使用原始变量中15.6%的信息获得了比全变量PLS模型更好的鸭梨SSC含量预测结果,r2_pre,RMSEP和RPD分别为0.908 2,0.312 0和3.300 5。 进一步与基于蒙特卡罗无信息变量MC-UVE和遗传算法(GA)获得的特征变量建立的PLS模型比较发现,CARS不仅可以去除原始光谱数据中的无信息变量,同时也能够对共线性的变量进行压缩去除,该方法能够有效地用于高光谱数据变量的选择。 结果表明,近红外高光谱技术结合CARS-PLS模型能够用于鸭梨可溶性固形物SSC含量的定量预测。 从而为基于近红外高光谱技术预测水果内部品质的研究提供了参考。