典型矿物颜料混合像元几何单形体端元提取算法研究

利用高光谱像元解混方法识别彩绘文物表面复合颜料的类别已成为文物保护修复领域的研究热点,其中端元提取是关键的一个环节。首先选择中国绘画5种色系中具有代表性的矿物颜料,通过线性混合其反射光谱合成模拟高光谱图像;然后采用内部最大体积法、顶点成分分析、最小体积单形体分析、基于变分增广拉格朗日单形体识别以及最小体积约束的非负矩阵分解5种基于几何单形体的端元提取算法;最后在不同信噪比和是否有纯净像元的图像中提取混合颜料中的端元,并以光谱夹角距离和光谱信息散度作为评价指标,分别比较5种算法端元提取的精度。实验结果表明,基于变分增广拉格朗日单形体识别法和最小体积约束的非负矩阵分解法对混合颜料的端元提取效果较好,可为文物表面颜料解混提供参考。     

高光谱技术在书画检测中的应用研究——以王震《三秋图》为例

书画作品是人类文化遗产的重要组成部分。高光谱成像技术具有"图谱合一"以及无损检测的特点,本文结合拉曼光谱和荧光光谱,利用高光谱成像技术对王震《三秋图》进行分析,主要研究颜料识别、受损提取和隐含信息挖掘。实验不仅对画作中的颜料进行了分析,而且运用主成分分析方法与SAM分类等算法对其进行图像增强与特征提取,实现了书画隐含信息的挖掘。实验结果表明高光谱技术将会是书画研究的一种有效方法。     

基于高光谱影像的瞿昙寺壁画颜料层脱落病害评估

瞿昙寺因寺内所藏巨幅彩色壁画而闻名,但由于受到自然因素及人为因素的影响,寺内51间壁画廊中有些壁画已经发生褪色、龟裂、卷翘、起甲、颜料层脱落等病害,尤其是颜料层一旦脱落,对于不可再生的壁画来说,损失无法弥补。因此在壁画的保护修复过程中,对壁画病害的识别与评估就变得十分关键。以瞿昙寺西回廊的高光谱图像数据为研究数据,利用支持向量机(Support Vector Machine,SVM)方法对壁画病害识别分类;以此提取的病害矢量数据为基础,构建壁画病害的评估指标要素,进而提出基于多元统计回归的LS-SR-AIC评估模型,实现壁画病害严重程度的评估。实验结果表明:利用该方法颜料层脱落病害的SVM提取精度达95%以上,同时为颜料层脱落病害工程评价提供了一种新的方法指导,为文物的健康评价走向定量化评估提供参考与借鉴。     

三江源区毒杂草型退化草地植被光谱特征分析

毒草型退化草地具有群落演替特点,通过高光谱遥感技术反演毒杂草分布与退化草地群落结构能对该类退化草地进行有效监测,而光谱特征分析是毒杂草与优良牧草遥感识别的基础。本文选取了三江源区毒草型退化草地的8种典型毒杂草和4种优良牧草的地面实测高光谱数据作为研究样本,经过SG平滑、包络线去除、导数变换和光谱参量化对毒杂草种和优良牧草种的光谱特征进行了分析,并通过马氏距离法提取其特征识别波段。结果表明：① 8种典型毒杂草和4种优良牧草的 “近红外峰值”差异较大,其中鹅绒萎陵菜的“近红外峰值”达到60.07%,而最小者早熟禾仅为17.53%;② 经包络线去除处理后,植被光谱曲线中吸收谷和反射峰光谱差异更加明显,且可减少环境背景对植被光谱的影响,如沼泽草甸的鹅绒委陵菜和驴蹄草,其“绿峰幅值”分别为6.46%和6.89%,经处理后其“绿峰指数”分别为0.2866和0.3671,而在2种环境下生长的同一草种（狼毒草1和狼毒草2）的峰谷特征差异不明显;③ 基于马氏距离法提取的毒杂草与优良牧草的敏感识别波段主要分布在680~750 nm和900~1000 nm波长范围内,以醉马草与矮嵩草为例,其基于反射率的敏感识别波段为713.1~737.1 nm和934.6~965.6 nm。该研究可为利用高光谱遥感进行大面积毒杂草草种识别和植被群落生长监测提供重要科学依据,对于三江源区毒杂草的监测防治和畜牧业的可持续发展具有重要意义。     

高光谱遥感的物质组分和物质成分反演的应用分析

高光谱遥感的最大特点是可以提取和重建像元光谱,从而依据光谱特征直接识别地物类型、地物组成,乃至地物的成分,反演地物的物理、化学参量。本文阐述了高光谱的光谱识别方法所能探测的岩石、土壤、植被和人工建筑物的物质组分或成分;另外,与多光谱相比较的角度,讨论了高光谱图像地物特征识别和物质成分反演等技术方法的应用。     

基于深度置信网络的CRISM影像火星表面矿物识别方法

鉴于传统的光谱特征参数方法存在不能综合考虑光谱在整个波长范围内的谱形、对于单一吸收带相似的不同矿物难以区分等问题,研究采用深度置信网络方法对火星专用小型侦察影像频谱仪（CRISM）高光谱影像中的火星表面矿物进行自动识别,该算法具体包括:①预训练阶段。利用非监督算法逐层训练受限玻尔兹曼机,自动学习模型参数,提取光谱特征。②调优阶段。将自动学习的光谱特征输入分类器,采用反向传播算法对模型进行监督微调,识别矿物在CRISM影像中的分布。在算法的研究中,采用光谱比值方法降低火星表面灰尘等噪声对矿物光谱的影响,并探讨样本数量、隐含层节点数、网络深度等对算法识别精度的影响,试图构建适宜于CRISM影像火星表面矿物识别的深度置信网络模型。以火星表面镁铁蒙脱石和氯盐为例进行测试,实验结果表明:该方法能够对火星表面矿物进行自动识别,准确率达到85%以上,与光谱参数法的识别结果基本叠合,并能够探测光谱参数法未能识别的部分矿物分布。      

基于多元参照光谱的SAM岩性划分方法

光谱角制图（SAM）是一种光谱匹配方法,通过量化目标地物光谱与已知参照光谱的相似程度来判断目标地物的类型,广泛应用于多光谱遥感岩性识别。目前,岩石单元训练区内像元的均值光谱,往往作为代表该岩石单元的参照光谱来进行光谱角制图,而使用均值光谱作为参照光谱库的方法,并未考虑岩石单元内部固有的光谱差异性,很大程度上影响了SAM分类效果。为了消除SAM岩性划分时岩石单元内部的光谱差异的影响,本文采用了一种多元参照光谱的SAM岩性划分方法。首先,依据研究区已有地质资料和Landsat-8影像特征,选定各类岩石单元训练区;接着进行各岩石单元训练区内部像元的光谱差异性分析,和各训练区之间的样本可分离性分析,分别使用均值参照光谱库的SAM方法和多元参照光谱库的SAM方法对研究区Landsat-8影像进行了岩性划分实验。研究结果表明,多元参照光谱库的SAM岩性分类方法很好地改善了岩石单元内部光谱差异性对均值参照光谱库的SAM岩性分类方法的影响,分类精度显著提高。     

基于光谱-空间残差网络模型的高光谱遥感图像分类

残差网络是近几年提出的一种新型深度卷积网络,通过增加网络深度提高分类的准确率,也解决了网络退化问题。基于残差学习原理,设计了针对高光谱遥感图像分类的光谱-空间残差网络模型。首先,将原始高光谱遥感数据三维立方体输入网络模型,并使用特定的卷积核对光谱特征进行降维;然后,利用光谱残差模块和空间残差按模块分别且连续地学习光谱和空间特征;最后,对提取到的特征进行池化操作并分类。此外,为规范训练数据和防止过拟合,学习过程中使用了批量归一化和dropout的方法。所设计网络模型在Indian Pines和Pavia U数据集上进行了验证实验,结果表明,所提方法有效地缓解了网络退化的问题,且在分类精度上也高于支持向量机、卷积神经网络等现有算法。     

面向高光谱矿物填图的多特征结合降维方法研究

高光谱影像具有图谱合一的特点,图像空间信息是遥感影像的重要信息,但以往基于最佳波段选择的降维方法中只考虑基于灰度统计的特征空间信息,忽视了图像空间信息,而且计算量大。综合高光谱遥感影像的特征空间与图像空间信息,提出了一种多特征结合的高光谱影像降维方法并应用于矿物填图中。统计分析波段相关性并划分不同特征子空间;计算各波段的分形维数,在各子空间选择分形维数较小的波段作为候选波段;在候选波段中,计算待识别地物光谱间的相关系数,并快速选择出最佳波段组合。经实验,应用该方法选出的最佳波段组合影像清晰、不同蚀变矿物对比明显,根据特征选择提取出的矿物蚀变信息与应用成熟的光谱角制图(SAM)提取结果大致相同,表明结合图像空间和特征空间的降维方法能够选择出理想的波段组合,有效降低高光谱数据的维数,信息提取效果好。      

面向对象的航空高光谱图像混合分类方法

传统的高光谱分类通常仅考虑单一像元的光谱或纹理特征,分类后容易出现地物破碎的现象。鉴于此,本文提出了一种面向对象的混合分类方法,将面向对象的分割结果与传统的像元级分类结果进行有机融合,充分利用对象的光谱特征和空间结构特征。在此基础上,引入了2种具体的混合分类方法,即多尺度分割的SVM分类和多波段分水岭分割的SVM分类。前者将地物光谱的可变性进行弱化处理,转化为多尺度均质对象单元进行分类;后者融入了地物的空间信息和形态学特征,对分割得到的同质区域进行分类。将这2种分类方法应用于航空高光谱数据,实验结果表明：面向对象的混合分类方法的总体精度分别为92.63%和96.13%,与传统的像元级分类法相比,分别提高了10.14%和13.64%,有效地解决了分类后地物的破碎现象。     

矿物岩石高光谱数据库分析

USGS-MIN、JPL、JHU、IGCP-264、ASTER等是当前国际上几个常用的光谱数据库。本文通过分析这些光谱库数据,发现某些矿物的光谱在不同光谱库中有较大的差异,甚至在同一光谱库中也有多种曲线形态。由此进一步分析并验证了由矿物组成的各类岩石在光谱特征上存在更大的变异。这些差异都会给高光谱遥感图像分析带来不利影响。文中讨论了影响矿物、岩石光谱特征的几个因素,并指出了建立光谱库的注意事项。     

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一种基于频谱分析和AR补偿的对流层延迟预报模型

利用实测天顶对流层延迟值（ZTD）,在无气象参数条件下,提出一种对流层延迟建模与预报方法。首先利用频谱分析,得到ZTD时间序列周期特性,并在此基础上对ZTD进行建模,给出模型参数计算方法。然后,基于频谱分析模型拟合ZTD,并与实测值进行比较分析。最后,将分析得到的拟合残差用AR模型进行改正并预报。结果表明,基于频谱分析和AR补偿的ZTD改正预报模型能够满足不同测试环境下的需求,精度达到cm级。     

草地退化杂类草入侵遥感监测方法研究进展

近年来,受全球气候变暖及日趋频繁的人类活动的共同影响,我国草地生态系统出现了不同程度的退化,成为草地生态学界的研究热点。本文较系统总结了草地退化、杂类草入侵遥感监测方法的应用进展,指出了常规草地退化遥感监测方法的不足,并展望了今后的发展方向。研究发现草地退化在群落尺度上往往表现为优质牧草种类减少,杂类草种类增加。在草地退化过程中,杂类草侵入,往往导致植被覆盖度增加,因而传统的基于植被覆盖度、生产力和产草量下降的草地退化遥感监测方法,无法反映草地退化在植被种群方面的变化特征,尤其是杂类草入侵的草地退化过程。而目前基于高光谱数据的草地退化杂类草入侵监测方法,结合地面光谱测定和对退化草地物种特征微弱光谱差异的定量分析,能有效识别群落中杂类草种类,并反演出占群落的面积比例、高度和盖度等,可为草地退化监测及其治理提供群落演替过程及其态势的重要指标,为草地退化监测提供新的遥感技术方法。     

Spectral response of different eroded soils in subtropical china: A case study in Changting County,China

Hyper-spectral data is widely used to determine soil properties. However, few studies have explored the soil spectral characteristics as response to soil erosion. This study analysed the spectral response of different eroded soils in subtropical China, and then identify the spectral characteristics and soil properties that better discriminate softs with different erosion degrees. Two methods were compared： direct identification by inherent spectral characteristics and indirect identification by predictions of critical soft properties. Results showed that the spectral curves for different degrees of erosion were similar in morphology, while overall reflectance and characteristics of specific absorption peaks were different. When the first method is applied, some differences among different eroded groups were found by integration of associated indicators. However, the index of such indicators showed apparent mixing and crossover among different groups, which reduced the accuracy of identification. For the second method, the correlation between critical soil properties, such as soil organic matter （SOM）, iron and aluminium oxides and reflectance spectra, was analysed. The correlation coefficients for the moderate eroded group were primarily between -0.3 to -0.5, which were worse than the other twogroups. However, the maximum value of R2 was obtained as 0.86 and 0.94 for the non-apparent eroded and the severe group. Furthermore, these two groups also showed some differences in the spectral response of iron complex state （Fep）, Aluminium amorphous state （Alo） and the modelling results for soil organic matter （SOM）. The study proved that it is feasible to identify different degrees of soil erosion by hyperspectral data, and that indirect identification by modelling critical soil properties and reflectance spectra is much better than direct identification. These results indicate that hyper-spectral data may represent a promising tool in monitoring and modelling soil erosion.     

Gaussian decomposition and component pigment spectral analysis of phytoplankton absorption spectra

The absorption spectrum of phytoplankton is an important bio-optical parameter for ocean color hyperspectral remote sensing;its magnitude and shape can be affected considerably by pigment composition and concentration.We conducted Gaussian decomposition to the absorption spectra of phytoplankton pigment and studied the spectral components of the phytoplankton,in which the package effect was investigated using pigment concentration data and phytoplankton absorption spectra.The decomposition results were compared with the corresponding concentrations of the five main pigment groups(chlorophylls a,b,and c,photo-synthetic carotenoids(PSC),and photo-protective carotenoids(PPC)).The results indicate that the majority of residual errors in the Gaussian decomposition are0.001 m~(-1),and R~2 of the power regression between characteristic bands and HPLC pigment concentrations(except for chlorophyll b)was0.65 or greater for surface water samples at autumn cruise.In addition,we determined a strong predictive capability for chlorophylls a,c,PPC,and PSC.We also tested the estimation of pigment concentrations from the empirical specific absorption coefficient of pigment composition.The empirical decomposition showed that the Ficek model was the closest to the original spectra with the smallest residual errors.The pigment decomposition results and HPLC measurements of pigment concentration are in a high consistency as the scatter plots are distributed largely near the 1:1 line in spite of prominent seasonal variations.The Wozniak model showed a better fit than the Ficek model for Chla,and the median relative error was small.The pigment component information estimated from the phytoplankton absorption spectra can help better remote sensing of hyperspectral ocean color that related to the changes in phytoplankton communities and varieties.     

Isolation and characterization of melanin pigment from yesso scallop <Emphasis Type="Italic">Patinopecten yessoensis</Emphasis>

Melanin is one of the essential compounds in the pigments of molluscan shells. However, the effects of melanin on color variations in molluscs are largely unknown. Our previous study suggests that Yesso scallop Patinopecten yessoensis might contain melanin pigment in the dark brown shell. We therefore isolated melanin from the pigmented shells using hydrochloric acid method, and characterized the types of melanin pigments by spectrophotometry. The purified melanin, which was verified by spectrophotometry scanning and HPLC analysis, showed the typical characteristics of melanin absorption spectra and HPLC chromatograms. The contents of pheomelanin and eumelanin in pigmented shells, which were determined by the linear standard curve of melanin at 405 nm and 350 nm absorbance, were 48.23 ± 1.350 and 157.65 ± 5.905 mg, respectively. The present results indicate that the brown-pigmented shells of scallops comprise approximately 76.6% of eumelanin and 23.4% of pheomelanin, which supports the presence of eumelanin-rich pigment in scallop shells. Therefore, the combination of hydrochloric acid extraction and spectrophotometric quantification is a rapid and efficient method to isolate and quantify melanin in shells. This will facilitate the melanin studies related to shell color polymorphism and the selective breeding of bivalves with different shell colors.     

Isolation and Characterization of Melanin Pigment from Yesso Scallop Patinopecten yessoensis

Melanin is one of the essential compounds in the pigments of molluscan shells.However,the effects of melanin on color variations in molluscs are largely unknown.Our previous study suggests that Yesso scallop Patinopecten yessoensis might contain melanin pigment in the dark brown shell.We therefore isolated melanin from the pigmented shells using hydrochloric acid method,and characterized the types of melanin pigments by spectrophotometry.The purified melanin,which was verified by spectrophotometry scanning and HPLC analysis,showed the typical characteristics of melanin absorption spectra and HPLC chromatograms.The contents of pheomelanin and eumelanin in pigmented shells,which were determined by the linear standard curve of melanin at 405 nm and 350 nm absorbance,were 48.23 ? 1.350 and 157.65 ? 5.905 mg,respectively.The present results indicate that the brown-pigmented shells of scallops comprise approximately 76.6% of eumelanin and 23.4% of pheomelanin,which supports the presence of eumelanin-rich pigment in scallop shells.Therefore,the combination of hydrochloric acid extraction and spectrophotometric quantification is a rapid and efficient method to isolate and quantify melanin in shells.This will facilitate the melanin studies related to shell color polymorphism and the selective breeding of bivalves with different shell colors.     

基于偏振植被指数的植被-土壤混合像元室内实验研究

混合像元是遥感影像中普遍存在的一种现象,对其组成和各成分比例的反演一直是遥感研究中的重难点,而国内利用偏振植被指数对混合像元的研究几乎没有涉及。本次研究通过对不同面积比例的植被-土壤混合像元偏振反射高光谱特征进行分析,讨论不同条件下的植被-土壤混合像元偏振高光谱特性,并利用偏振反射比计算了12种0°偏振态下的偏振植被指数,分别构建了植被指数与植被面积比例以及光谱特征参数与植被面积比例的数学模型。结果表明,混合像元中植被面积比例和偏振角均对其偏振高光谱有一定的影响;865 nm的偏振反射比与植被占像元面积比例的相关性最好,采用多项式进行拟合时,其决定系数达到0.99,适合进行植被占像元面积比例的反演;偏振光谱“红边”处的一阶微分值与植被像元比例存在良好的线性相关,R²=0.974;植被面积比例与植被指数和光谱特征参数呈现良好的相关关系,其中P-DVI和光谱吸收指数（SAI）与植被面积比例的拟合效果最好,决定系数分别为0.99以及0.94,适合进行植被-土壤混合像元中植被面积比例的反演。