水稻分蘖期无人机高光谱影像混合像元特征分析与分解

开展水稻无人机高光谱解混,获取水稻植株的高光谱反射率信息,对于提高水稻理化参量的反演模型精度具有重要意义.目前大多基于高光谱遥感影像自身数据进行解混,运用算法模型进行高光谱数据解混,将高光谱图像和可见光图像进行优势互补,提出一种基于无人机高清影像与高光谱遥感影像融合的稻田无人机高光谱解混方法,解决单一数据局限性问题,增...     

一种月壤主要矿物组分含量反演的光谱解混方法

从月壤光谱中挖掘矿物含量信息是月球遥感探测的重要内容之一,矿物光谱混合规律复杂,光谱特征对测试环境、粒度、地形、背景极为敏感是难点。提出并试验了一种基于光谱分解反演矿物含量的方法。在光谱分解前采用光谱连续统去除突出光谱谱形,分解中综合运用了光谱反射峰(介于两个吸收谷之间的光谱曲线向上凸起的部分)与吸收谷,分解后采用Hapke模型修正光谱非线性混合的影响,在混合端元光谱、端元化学成分与粒度未知情况下,实现了对月壤四种主要矿物橄榄石、斜方辉石、单斜辉石、斜长石的43条混合光谱的盲分解与矿物含量反演,反演的残差、误差、线性拟合相关系数(反演含量与真实含量)均值分别为5.0 Vol%,14.4 Vol%,0.92。该方法与反演结果可为月球高光谱矿物识别与填图提供参考与依据。     

Raman labeled nanoparticles: characterization of variability and improved method for unmixing

Raman spectroscopy can differentiate the spectral fingerprints of many molecules, resulting in potentially high multiplexing capabilities of Raman‐tagged nanoparticles. However, an accurate quantitative unmixing of Raman spectra is challenging because of potential overlaps between Raman peaks from each molecule, as well as slight variations in the location, height, and width of very narrow peaks. If not accounted for properly, even minor fluctuations in the spectra may produce significant error that will ultimately result in poor unmixing accuracy. The objective of our study was to develop and validate a mathematical model of the Raman spectra of nanoparticles to unmix the contributions from each nanoparticle allowing simultaneous quantitation of several nanoparticle concentrations during sample characterization. We developed and evaluated an algorithm for quantitative unmixing of the spectra called narrow peak spectral algorithm (NPSA). Using NPSA, we were able to successfully unmix Raman spectra of up to seven Raman nanoparticles after correcting for spectral variations of 30% intensity and shifts in peak locations of up to 10 cm⁻¹, which is equivalent to 50% of the full width at half maximum (FWHM). We compared the performance of NPSA to the conventional least squares (LS) analysis. Error in the NPSA is approximately 50% lower than in the LS. The error in estimating the relative contributions of each nanoparticle with the use of the NPSA are in the range of 10–16% for equal ratios and 13–19% for unequal ratios for the unmixing of seven composite organic–inorganic nanoparticles (COINs); whereas, the errors from using the traditional LS approach were in the range of 25–38% for equal ratios and 45–68% for unequal ratios. Here, we report for the first time the quantitative unmixing of seven nanoparticles with a maximum root mean square of the percentage error (RMS%) error of less than 20%. Copyright © 2012 John Wiley & Sons, Ltd.     

基于主成分分析-二阶导数光谱成像的红外显微图像分析

红外显微成像技术将红外光谱技术和显微技术相结合,不仅可以提供测试样品的光谱信息而且能够提供测试样品的空间分布信息。然而复杂样品的红外显微图像谱峰重叠严重,无法直接获得目标组分的分布信息。将因子分析与光谱分离技术相结合提出了主成分分析-二阶导数光谱成像方法。通过兔子动脉红外显微图像中胆固醇分布的成像实验,验证该方法的可行性和有效性。实验结果表明,该方法可以提高光谱分辨率,挖掘隐藏于重叠谱峰中的有用信息,是一种有效的红外显微图像分析方法。     

Simple approach to one-laser, broadband coherent anti-Stokes Raman scattering microscopy

Coherent anti-Stokes Raman scattering (CARS) microscopy is emerging as a powerful method for imaging materials and biological systems, partly because of its noninvasiveness and selective chemical sensitivity. However, its full potential for species-selective imaging is limited by a restricted spectral bandwidth. Recent increases in bandwidth are promising but still are not sufficient for the level of robust component discrimination that would be needed in a chemically complex milieu found, for example, in intracellular and extracellular environments. We demonstrate a truly broadband CARS imaging instrument that we use to acquire hyperspectral images with vibrational spectra over a bandwidth of 2500 cm(-1) with a resolution of 13 cm(-1).     

Quantitative,comparable coherent anti‐Stokes Raman scattering (CARS) spectroscopy: correcting errors in phase retrieval

Coherent anti‐Stokes Raman scattering (CARS) microspectroscopy has demonstrated significant potential for biological and materials imaging. To date, however, the primary mechanism of disseminating CARS spectroscopic information is through pseudocolor imagery, which explicitly neglects a vast majority of the hyperspectral data. Furthermore, current paradigms in CARS spectral processing do not lend themselves to quantitative sample‐to‐sample comparability. The primary limitation stems from the need to accurately measure the so‐called nonresonant background (NRB) that is used to extract the chemically sensitive Raman information from the raw spectra. Measurement of the NRB on a pixel‐by‐pixel basis is a nontrivial task; thus, surrogate NRB from glass or water is typically utilized, resulting in error between the actual and estimated amplitude and phase. In this paper, we present a new methodology for extracting the Raman spectral features that significantly suppresses these errors through phase detrending and scaling. Classic methods of error correction, such as baseline detrending, are demonstrated to be inaccurate and to simply mask the underlying errors. The theoretical justification is presented by re‐developing the theory of phase retrieval via the Kramers–Kronig relation, and we demonstrate that these results are also applicable to maximum entropy method‐based phase retrieval. This new error‐correction approach is experimentally applied to glycerol spectra and tissue images, demonstrating marked consistency between spectra obtained using different NRB estimates and between spectra obtained on different instruments. Additionally, in order to facilitate implementation of these approaches, we have made many of the tools described herein available free for download. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.     

Tip-enhanced Raman spectroscopy and microscopy on single dye molecules with 15 nm resolution

Our recently developed approach of UHV-tip-enhanced Raman spectroscopy permits us to acquire Raman spectra of a few single brilliant cresyl blue (BCB) molecules and even a single one adsorbed on a Au(111) surface. This is substantiated by simultaneously recorded STM images. Furthermore, because of the reduced photobleaching in UHV, the time frame for spectral acquisition is sufficiently extended to allow tip-enhanced Raman imaging of a single BCB molecule with a lateral resolution of 15 nm.     

Comparison of In Vivo Optical Systems for Bioluminescence and Fluorescence Imaging

In vivo optical imaging has become a popular tool in animal laboratories. Currently, many in vivo optical imaging systems are available on the market, which often makes it difficult for research groups to decide which system fits their needs best. In this work we compared different commercially available systems, which can measure both bioluminescent and fluorescent light. The systems were tested for their bioluminescent and fluorescent sensitivity both in vitro and in vivo. The IVIS Lumina II was found to be most sensitive for bioluminescence imaging, with the Photon Imager a close second. Contrary, the Kodak system was, in vitro, the most sensitive system for fluorescence imaging. In vivo, the fluorescence sensitivity of the systems was similar. Finally, we examined the added value of spectral unmixing algorithms for in vivo optical imaging and demonstrated that spectral unmixing resulted in at least a doubling of the in vivo sensitivity. Additionally, spectral unmixing also enabled separate imaging of dyes with overlapping spectra which were, without spectral unmixing, not distinguishable.     

Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

A methodology of multivariate chemometric techniques based on the information‐theoretic approach was applied for elucidating chemical reaction information from a Raman data array R _m×ν that arises from in situ reaction monitoring. This reaction‐induced dynamic dataset R _m×ν can be contaminated by random cosmic ray spikes found in the midst of characteristic spectral variations associated with the disappearance or emergence of Raman active reactants, intermediates and products. Such spurious cosmic spikes were identified and removed using a novel and fast numerical approach based on maximum and minimum spectral entropy principles while preserving the genuine reaction‐induced spectral variations. Subsequently, the band‐target entropy minimization (BTEM) algorithm, a minimum spectral entropy based self‐modeling curve resolution technique, was applied to recover the pure component spectra of Raman active chemical species. Information gain through the chemometric analyses was calculated using information entropies with base 2 logarithm. This sequence of information‐theoretic chemometric analyses (or transinformations) was successfully tested on the reaction spectral data obtained from alcoholysis of acetic anhydride, which contains four Raman active chemical species. It is envisioned that this series of multivariate statistical analyses will be useful in chemical reaction studies and process analytical technology (PAT) applications that utilize in situ Raman spectroscopy to monitor transient dynamic changes in chemical concentrations, and also in Raman microscopy/imaging data containing spatial variations. Copyright © 2010 John Wiley & Sons, Ltd.     

Quantitative spectrally resolved imaging through a spectrograph

A grating spectrograph can be used for spectrally selective two-dimensional imaging if it is operated with a broad entrance slit. The resulting intensity distribution in its exit plane is a one-dimensional convolution of the spatial and spectral distributions of incident light. We present a dedicated deconvolution filter to reconstruct the spatial image from the spectrograph output. The algorithm is illustrated on Raman imaging of an underexpanded dry air jet. Recorded Raman images correspond to density maps convolved with the Raman spectrum of air; the latter essentially acts as a blurring function for the density map. The deconvolution filter combines the individual images recorded in the O2 and N2 Raman bands into a single image of relative air density.     

基于平均吸收的太赫兹波振幅成像研究

提出了一种基于一定频率内平均吸收的太赫兹(THz)波振幅成像新方法。太赫兹波频率在0.1～10 THz之间,波段位于红外和微波之间。太赫兹波成像技术的一个显著特点是信息量大,如何对每个样品点的大量信息进行处理提取有用信息重构出样品的图像是一项关键技术。选用中间挖空有“THz”字样的白纸为样品作太赫兹波成像研究,首先探讨了时域和频域上几种常用太赫兹波振幅成像方法所反映的样品信息及其特点,进一步使用提出的基于一定频率内平均吸收的太赫兹波振幅成像新方法对样品进行图像重构。实验结果表明这种新方法可以很好的反映样品的真实信息,反映了样品在一定频率范围内由于吸收而引起的效果的综合,与吸收系数和厚度相关,离散效应得到了很好的消除,相对几种常用的太赫兹波振幅成像方法能够得到更清晰的图像。此新方法尤其适用于结构简单的样品,能够成为几种常用振幅成像方法的有力补充。     

古代多色硅酸盐制品的光谱学分析及方法学研究

采用自主搭建的多光谱成像系统、便携式X射线荧光光谱仪(X-ray fluorescence spectrometer, XRF)、激光拉曼光谱仪(laser Raman spectrometer, LRS)对5件战国时期多色硅酸盐制品的表面物质的光谱特性、化学成分和物相结构进行了无损分析。根据多光谱图像中的光谱响应情况,对样品表面物质进行了区域划分,并发现样品表面的多数蓝、绿、紫色系区域出现荧光现象。XRF检测结果表明不同区域的化学成分存在明显差异,但多数区域均以SiO₂,PbO,BaO为主要化学成分,5件样品基本属于铅钡硅酸盐体系,而且出现荧光区域皆是铜离子致色。LRS精确检测出各个区域中的玻璃相、中国蓝、中国紫、石英、赤铁矿、铅白、无定形碳等物相,并发现产生可见发光二极管致近红外荧光的物质为中国蓝和中国紫。利用X射线衍射技术(X-ray diffraction, XRD)验证了LRS检测结果的正确性。本研究将多用于书画分析的多光谱成像面检测技术与常用于硅酸盐文物成分分析的XRF和LRS技术进行有效结合,提出了一套更为高效的无损的分析古代多色硅酸盐制品的研究方法。光谱图像与X射线荧光光谱、激光拉曼光谱数据的结合,将样品的光谱特性与化学构成联系起来,有助于提高硅酸盐文物的分析效率,加强对硅酸盐文物的整体认识,和降低硅酸盐文物的受损风险。     

Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging

Chemical imaging method of vibrational spectroscopy, which provides both spectral and spatial information, creates a three‐dimensional (3D) dataset with a huge amount of data. When the components of the sample are unknown or their reference spectra are not available, the classical least squares (CLS) method cannot be applied to create visualized distribution maps. Raman image datasets can be evaluated even in such cases using multivariate (chemometric) methods for extracting the needed hidden information. The capability of chemometrics‐assisted Raman mapping is evaluated through the analysis of pharmaceutical tablets (considered as unknown) with the aim of estimating the pure component spectra based on the collected Raman image. Six chemometric methods, namely, principal component analysis (PCA), maximum autocorrelation factors (MAF), sample–sample 2D correlation spectroscopy (SS2D), self‐modeling mixture analysis (SMMA), multivariate curve resolution–alternating least squares (MCR‐ALS), and positive matrix factorization (PMF), were compared. SMMA was found to be the best choice to determine the number of components. MCR‐ALS and PMF provided the pure component spectra with the highest quality. MCR‐ALS was found to be superior to PMF in the estimation of Raman scores (which correspond to the concentrations) and yielded almost the same results as CLS (using the real reference spectra). Thus, the combination of Raman mapping and chemometrics could be successfully used to characterize unknown pharmaceuticals, identify their ingredients, and obtain information about their structures. This may be useful in the struggles against illegal and counterfeit products and also in the field of pharmaceutical industry when contaminants are to be identified. Copyright © 2011 John Wiley & Sons, Ltd.     

Hyperspectral unmixing based on ISOMAP and spatial information

Several nonlinear techniques have recently been proposed for classification and unmixing applications in hyperspectral image processing. A commonly used data-driven approach for treating nonlinear problems employs the geodesic distances on the data manifold as the property of interest. Although this approach often produces better results than linear unmixing algorithms, the graph-based method treats an image as a bag of spectral signatures and ignores the relationship between the pixel and its spatial neighbors. To utilize the spatial distribution of pixels and improve hyperspectral unmixing precision effectively, a new method is proposed for incorporating nonlinear dimension reduction and spatial information, using isometric mapping (ISOMAP) to find significant low-dimensional structures hidden in high-dimensional hyperspectral data. Spatial information is also introduced into the traditional spectral-based endmember search process. A fully constrained least-squares algorithm is used to evaluate the abundance of each endmember. The experimental results for actual images reveal that the performance of the proposed method obtains much better unmixing results than the classical N-FINDR and ISOMAP algorithms.     

基于拉曼成像技术的面粉中抗坏血酸添加剂定量检测研究

抗坏血酸是一种常见的面粉品质改良剂,用于改善面团的流变学特性及面包的烘焙品质。本研究以面粉中含不同浓度抗坏血酸的混合样品为研究对象,通过拉曼成像技术实现面粉中抗坏血酸的检测、识别和定量分析。分别采集面粉、抗坏血酸和面粉-抗坏血酸混合样品的拉曼图像,确定感兴趣区域及光谱范围,以抗坏血酸拉曼光谱中强度较高且区别于面粉的3处拉曼峰（631,1 128和1 658 cm-1）为依据对混合样品的平均拉曼光谱进行分析,结果显示其不能有效评估面粉中抗坏血酸含量,研究探索对图像中各像素点对应的拉曼光谱进行分析以实现面粉中抗坏血酸的有效检测。以混合样品图像中各像素点拉曼光谱作为校正集、面粉平均拉曼光谱和抗坏血酸平均拉曼光谱的线性组合光谱作为验证集建立偏最小二乘模型,模型的回归系数用于将混合样品的三维拉曼图像重建为二维灰度图像,通过阈值分割实现面粉中抗坏血酸的检测和识别,根据识别结果建立定量分析模型。结果显示,偏最小二乘模型的最高和最低回归系数分别对应于抗坏血酸和面粉的最强拉曼峰,所有回归系数应用于混合样品拉曼图像将其转换为灰度图像后面粉和抗坏血酸的像素点仍难以识别,阈值分割方法将灰度图像转换为用于分类面粉像素和抗坏血酸像素的二值图像实现了面粉中抗坏血酸的有效检测。通过分析各浓度混合样品对应子样品中识别到的抗坏血酸像素点数确定本研究对面粉中抗坏血酸的最低检测浓度为0.01%（100 mg·kg-1）,混合样品中抗坏血酸浓度同图像中识别到的抗坏血酸像素点在0.01%~0.20%范围内具有良好的线性关系,决定系数为0.996 0。研究结果可为面粉中抗坏血酸添加剂的定量检测提供方法支持,为大规模快速筛查提供了技术参考。     

拉曼光谱检测生物大分子损伤的研究进展

拉曼光谱是基于拉曼散射效应而发展起来的一种光谱分析技术,体现的是分子的振动或转动信息。由于拉曼光谱技术与常规化学分析技术相比,具有对样品无损、样品制备简单和所需样品量少等特点,广泛用于生物大分子结构变化的研究。拉曼光谱不仅可以用于蛋白质、核酸和脂类等生物大分子损伤的快速检测,而且可以用于癌症的诊断与手术治疗。通过对比正常组织与癌变组织的拉曼光谱,可以找到两种组织特征吸收峰的差异,从而为癌症的最终确诊和确定肿瘤切除范围提供重要信息。文章综述了拉曼光谱检测生物大分子损伤的研究进展,介绍了利用表面增强拉曼光谱、傅里叶变换拉曼光谱和紫外共振拉曼光谱等技术在检测蛋白质二级结构、膜脂及DNA损伤中的应用,并展望了未来拉曼光谱技术的发展前景。     

Confocal fluorescence polarization microscopy for linear unmixing of spectrally similar labels

Studies of biological samples often call for simultaneous identification of multiple molecular or structural components. Multiple labelling fluorescence techniques are a powerful way of achieving this. However, the ability to distinguish a number of fluorescent probes unambiguously can be restricted by the fact that fluorescence spectra are generally broad and overlapping. Recently a technique known as linear unmixing has been combined with spectral imaging to discriminate between multiple fluorophores. In this study a scheme is proposed whereby fluorescence polarization information is used to expand the capability of the linear unmixing technique to accommodate additional fluorescent probes. As a proof-of-concept, it is shown that this polarization-based technique can be used to divide the signals generated by two spectrally similar fluorescent probes into their separate components.     

Studies on paint cross sections of a glass painting by using FT‐IR and Raman microspectroscopy supported by univariate and hierarchical cluster analyses

Fourier transform infrared (FT‐IR) and Raman spectroscopy is used for the non‐destructive analysis of painting materials and ageing compounds in micrometric cross sections of a glass painting. The combination of both techniques in conjunction with imaging/mapping function provides the spatial distribution of chemical components identified in vibrational spectra. The aim of our work is to show the applicability of the FT‐Raman mapping technique in the detection of painting materials. We also compare Raman information gained by using two laser excitations at 532 and 1064 nm implemented in microspectrometers with different confocality and spatial resolution. In turn among FT‐IR imaging techniques, we compare chemical images recorded in external reflection and attenuated total reflection modes that give chemical images of different size and spatial resolution. Our FT‐IR and Raman imaging characterize a number of painting materials such as pigments, binders, fillers as well as degradation products. Raman maps are constructed by using the univariate analysis. In turn, a profile of IR images requires the use of a more complex methodology. Here, we compare FT‐IR images of the painting cross sections obtained by using the univariate and hierarchical cluster analysis. We clearly show that the multivariate approach is a powerful tool for the credible construction of IR images, providing the relevant chemical information on the multicomponent stratigraphy of the samples. Moreover, the combination of all the methods allows us to demonstrate their degree of utility for the study on the paint cross sections of the works of art. Copyright © 2013 John Wiley & Sons, Ltd.     

Deconvolution of pure component FT‐Raman spectra from thermal emission of barium sulfate and graphite samples using the BTEM algorithm

Band‐target entropy minimization (BTEM) was applied for the extraction of pure component Raman spectra from samples exhibiting a significant thermal background due to sample emission. In this method, singular value decomposition was first used to calculate the right singular vectors of the spectroscopic data matrix. Then the non‐noise right singular vectors were examined for localized spectral features, which were subsequently used for spectral recovery. These local features were targeted with the BTEM algorithm to recover the pure component Raman spectra. Accordingly, the interfering thermal background was removed. This method of analysis is applied to graphite and barium sulfate Raman spectroscopic data. Copyright © 2006 John Wiley & Sons, Ltd.