Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique

Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment.In combination of hyper-spectral technology and micro examination,the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth.The hyperspectral,chlorophyll content,net photosynthetic rate and stomatal conductance of leaf were obtained.The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope(ESEM).Normalized difference vegetation index(NDVI),modified red edge normalized(mNDVI705)and modified red edge simple ratio index(mSR705)were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band.16 methods were used to establish the physiological information inversion model.The main results were as follows:Under the influence of particulate matters,the spectral reflectance decreased as a whole.With the increase of leaf age,the phenomenon of blue shift aggravated.The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices.The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters.With longer affecting time and more deposition of particle matters on the leaf,the stomatal opening became smaller.After comparing,principal component regression(PCR)+multiple scatter correction(MSC)+second derivative(SD)+Savitzky-Golay smooth(SG),and partial least square(PLS)+multiple scatter correction(MSC)+first derivative(FD)+Savitzky-Golay smooth(SG)were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively.This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.

The recognition of ocean red tide with hyper-spectral-image based on EMD

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.

Hydrocarbon Micro-Seepage Detection by Altered Minerals Mapping from Airborne Hyper-Spectral Data in Xifeng Oilfield,China

Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting （SFF） in the loess coverage area of Xifeng Oflfield. The results re- veal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also re- vealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was pro- posed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro- seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffrac- tion （XRD） and atomic absorption spectrophotometer to validate the results. Within the extents of hydro- carbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.

High-resolution Hyper-spectral Image Classification with Parts-based Feature and Morphology Profile in Urban Area

High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.

Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.

Algorithm for retrieving surface pressure from hyper-spectral measurements in oxygen A-band

An algorithm for retrieving the surface pressure from oxygen A-band measurements in the future Chinese CO2satellite(CarbonSpec/TanSat)was developed.The ful physical radiative transfer model,vector radiative transfe model based on successive order of scattering,which i based on the successive order of scattering approach,wa used to simulate the measurements of CarbonSpec/TanSat as well as the kernel matrix in the inversion algorithm,and then the surface pressure and other related atmospheric parameters such as aerosol optical depth(AOD),surface albedo,and temperature were derived through optima estimation theory.Sensitivities of the algorithm to surface albedo,solar zenith angle(SZA),viewing zenith angle(VZA),aerosol type,and AOD were investigated,and the results showed that the absolute error of retrieved surface pressure increases with decreasing surface albedo o increasing SZA and VZA.An accuracy of\4 hPa ove bright surfaces(surface albedo C0.15)could be derived fo various SZAs and viewing geometries.Moreover,the algorithm can simultaneously retrieve the surface albedo AOD,and its vertical distribution indicated by scale

Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection

Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of source, an on-orbit radiometric calibration method is designed in this paper. This chain is based on the spectral inversion accuracy of the calibration light source. We compile the genetic algorithm progress which is used to optimize the channel design of the transfer radiometer and consider the degradation of the halogen lamp, thus realizing the high accuracy inversion of spectral curve in the whole working time. The experimental results show the average root mean squared error is 0.396%, the maximum root mean squared error is 0.448%, and the relative errors at all wavelengths are within 1% in the spectral range from 500 nm to 900 nm during 100 h operating time. The design lays a foundation for the high accuracy calibration of imaging spectrometer.

航空高光谱遥感技术在铀矿找矿中的典型应用——以新疆雪米斯坦地区为例

高光谱遥感技术是地质矿产勘查领域的重要新技术、新方法。近些年,航空高光谱遥感技术及其在铀矿找矿中的应用得到核地质系统的重视并开展深入研究。在分析前人铀矿找矿思路基础上,阐述了CASI/SASI航空高光谱遥感技术在新疆雪米斯坦地区的铀矿找矿新思路及其典型应用效果。首先深入剖析了白杨河铀矿床及其周围的航空高光谱遥感特征,识别了铀矿床产出的区域热液活动中心、铀成矿高铝绢云母蚀变等区域找矿关键要素,提出“区域热液活动中心识别-铀矿化航空高光谱蚀变组合发育地段确定-蚀变、构造、有利岩性等要素复合地段优选”的航空高光谱遥感铀矿找矿新思路;然后根据全区的航空高光谱遥感信息进行了找矿远景区筛选和野外查证,新发现了铀矿化异常。新发现的铀矿化异常地段航空高光谱遥感特征明显,地表铀异常显著,控矿断裂构造发育,地质环境有利,铀矿找矿前景良好。其是笔者对如何充分挖掘高光谱遥感铀矿找矿信息,提升高光谱遥感技术更好服务铀矿找矿应用效果的深入思考与探索,也是在地表岩石裸露程度良好的地区利用高光谱遥感技术直接寻找热液型矿产的良好例证,希冀为广大地质工作者开展找矿方法创新和应用提供参考。

基于光谱敏感变量优选的澳洲坚果叶片氮素含量估算

利用高光谱遥感技术探索澳洲坚果叶片氮素含量估算方法,以实现澳洲坚果氮素营养快速诊断。本研究以临沧和西双版纳为研究区,获取澳洲坚果品种O.C和HAES344叶片的光谱反射率和氮素含量,首先采用对数变换、导数变换及其组合对原始光谱反射率进行多种数学变换,然后分析澳洲坚果叶片氮素含量与不同变换形式光谱数据的相关性;在决定系数较大的原则下,选择决定系数曲线图中波峰特征点对应的波长作为氮素敏感波长,从而得到相应的氮素敏感光谱变量;运用逐步回归法对氮素敏感光谱变量进一步优化,并采用多元线性回归(MLR)、偏最小二乘回归(PLSR)和支持向量回归(SVR)3种方法构建澳洲坚果叶片氮素含量估算模型;最后,分别利用验证集和测试集对构建的澳洲坚果叶片氮素含量估算模型性能进行测试。结果显示,MLR、PLSR、SVR等3种模型估算能力均表现良好,验证集和测试集的相对分析误差(RPD)均在2.0以上;其中,PLSR模型为最优估算模型,验证集和测试集的RPD分别为2.099和2.110。从反射率(R)、对数变换(LR)、一阶导数(FDR)、对数变换的一阶导数(FDLR)、二阶导数(SDR)、对数变换的二阶导数(SDLR)等6种变换光谱数据中优选的19个氮素敏感光谱变量,对氮素光谱响应具有较强的稳定性;基于优选的19个氮素敏感光谱变量,用常规的回归建模方法均能取得良好的估算效果,且具有较强的区域普适性。本研究从多种变换光谱数据中优选氮素敏感光谱变量,为澳洲坚果叶片氮素含量估算提供新思路。

土壤有机质高光谱灰信息关联估测模型

为克服光谱估测中的不确定性,本文基于灰信息理论建立土壤有机质高光谱灰信息关联估测模型。以济南市章丘区的76个样本为基础,首先使用对数倒数的一阶微分、倒数对数的一阶微分等变换方法对光谱数据进行变换,计算相关系数,利用极大相关性原则选取估测因子。然后,根据增息取大法的原理,将每个样本的光谱估测因子进行从小到大的排序,形成灰信息量序列,基于信息链构建土壤有机质高光谱灰信息关联估测模型。最后,对基于不同信息链的估测结果进行两次融合处理,并与常用的估测方法进行对比分析。结果表明,12个检验样本的平均相对误差为5.576%,决定系数R2为0.934,估测精度高于多元线性回归、BP神经网络和支持向量机等常用方法。研究表明本文提出的灰信息关联模型是可行有效的,为土壤性状指标的高光谱估测提供了一种新途径。

基于Hyperion数据的普朗铜矿矿物信息提取分析

为突破普朗矿区海拔高、交通条件差、传统勘探方法受限的问题,使用高光谱遥感技术以更方便、准确地提取岩矿信息。利用Hyperion数据,采取最小噪声分离变换以及纯净像元指数法提取纯净像元作为端元,通过N维可视化分析法获得端元光谱;然后利用光谱角制图法提取研究区蚀变矿物,并与已知的地质资料结合分析,验证此方法的可行性。结果显示,高光谱遥感可以精确地识别蚀变矿物种类并显示分布范围,同时能够进行矿物填图,提高了蚀变信息的可利用性,对普朗铜矿区蚀变信息的遥感提取具有重要意义。

基于三波段光谱指数的春小麦叶片水分含量估算

为探讨利用三波段植被指数(three-band index,3BI)对春小麦叶片水分含量(leaf water content,LWC)估算的可行性,在田间尺度上,利用ASD-FieldSpec-3光谱仪测定春小麦抽穗期冠层光谱反射率,采用任意波段组合方法,分别建立两波段植被指数(two-band index,2BI)包括比值植被指数(RVI)、归一化植被指数(NDVI)、差值植被指数(DVI)及3BI,并对单波段反射率、两波段植被指数和三波段植被指数与春小麦抽穗期LWC之间进行相关性分析,筛选稳定的光谱参数,基于人工神经网络(artificial neural network,ANN)、K近邻(K-nearest neighbors,KNN)和支持向量回归(support vector regression,SVR)等3种机器学习算法,建立有效波段组合运算的抽穗期春小麦LWC估算模型,并利用独立样本对模型精度进行检验和评价。结果表明,单波段反射率、2BI和3BI与春小麦抽穗期LWC之间的相关性均达极显著水平(P<0.01),而相关系数差异较大,绝对值分别为0.23、0.62、0.94,说明组合波段展现了光谱隐含信息,避免有效光谱信息的丢失;估算模型中,春小麦抽穗期以KNN算法和最佳3BI组合变量(3BI-5(1075,1095,1085)、3BI-6(1100,400,1097))构建的模型拟合度最高(r^(2)=0.83),均方根误差最小(RMSE=2.14%),相对偏差百分比超出了2.0以上(RPD=2.31),说明该模型具有一定的预测能力。由此可见,通过任意波段组合,可明显提高3BI与春小麦LWC的关联度,且基于K近邻算法构建的模型具有较好的稳定性和估算能力。

一种高光谱图像中的桥梁检测方法

随着遥感技术的发展,高光谱图像的研究与应用逐渐进入加速期。作为一种常见的建筑物,桥梁在民用和军事方面都有具有重要意义。文章提出了一种基于高光谱图像特征的桥梁检测算法,该算法利用桥体轮廓的几何特性,将桥梁识别转换为检测图像中的两条平行直线,通过利用桥体的几何特性,成功实现了对高光谱图像中的桥梁检测。试验结果表明,桥梁有效检出率达98.31%,说明该算法能够有效检测高光谱图像中的桥梁。

黑土土壤水分高光谱特征及反演模型

应用高光谱技术阐释土壤含水率光谱规律及对土壤含水率进行定量分析,为精准农业地表土壤水分的快速测定提供参考。该文以吉林省黑土土类中的黑土亚类土壤为研究对象,利用ASD FieldSpec FR便携式光谱仪在室内环境下对不同含水率的土壤样本进行光谱反射率测量和特征分析;通过对土壤样品高光谱反射率进行对数、倒数、一阶微分以及反射率倒数的一阶微分、反射率对数的一阶微分变换,运用统计分析中的相关系数计算进行了光谱反射率与土壤水分的相关分析,并提取了土壤光谱特征波段;采用逐步多元线性回归方法和指数模式分析法,进行了高光谱土壤含水率定量反演。研究结果表明,在低于田间持水率状况下,黑土土壤光谱反射率及其反射率一阶微分和反射率对数一阶微分变换的敏感波段主要集中在400～410、1400～1850和2050～2200nm范围内,其中2156nm处与土壤水分相关系数最高,达0.89;在波长1328、1439、1742和2156nm处,采用反射率对数一阶微分所建立的黑土土壤含水率预测方程的预测精度最好,决定系数为0.931。黑土土壤含水率高光谱反演模型的建立为土壤水分的快速测定提供了新的途径。

基于高光谱荧光技术的叶菜农药残留快速检测

为了解决现有农药残留检测手段费时、复杂、前处理过程繁琐等不足,结合高光谱成像技术和荧光激发技术,搭建了高光谱荧光成像农药残留检测系统。在400～1100nm范围内获取叶菜表面毒死蜱的高光谱荧光图像,使用ENVI4.3软件提取了农药的荧光光谱信息。研究结果表明,毒死蜱在甲醇溶液中具有较强的荧光特性,在437nm附近产生荧光发射光谱,并且不同浓度的毒死蜱农药具有不同的荧光发射光谱峰值,随着农药浓度的降低其荧光特征峰值也降低。研究结果可为进一步开发和研究快速、精确的农药残留检测仪器提供理论依据。

基于高光谱遥感的小麦叶片氮积累量

作物氮素状况是评价长势、提高产量和改善品质的重要指标,因此叶片氮积累量的实时无损估测对作物生产的氮素管理具有重要意义。以多个小麦品种在不同施氮水平下的连续3 a大田试验为基础,研究了小麦叶片氮积累量与冠层高光谱参数的定量关系。结果表明,冠层叶片氮积累量随着施氮水平的提高而增加,光谱反射率在不同叶片氮积累量水平下发生相应的变化。叶片氮积累量的敏感波段主要存在于近红外平台和可见光区,其中,"红边"区域表现最为显著。通过微分等技术构造多种植被指数,对高光谱参数和叶片氮积累量间进行相关回归分析,SD r/SDb、FD742和AVHRR-GVI 3个参数与叶片氮积累量关系最密切,方程拟合决定系数R2分别为0.9163、0.9097和0.9142,估计标准误差SE分别为1.165、1.079和1.077。经不同年际独立数据的检验表明,利用光谱参数FD742建立的模型对叶片氮积累量的估测精度为0.8449,预测的RMSE为0.984;红边位置REPIG对叶片氮积累量的估测精度和预测的RMSE分别为0.8394和1.014,表明预测值与观察值之间符合精度高,比较而言,FD742为自变量建立的模型可以更好地评估不同条件下叶片氮素积累状况。

小麦叶片色素含量的高光谱监测

连续两年采用不同小麦品种在不同施氮水平下进行大田试验,建立了小麦叶片色素含量的光谱定量监测模型.结果表明,叶片色素含量随着施氮水平的增加而提高,品种间存在差异,叶绿素(Chl)a+b相对含量随生育时期的变化较Chlb和类胡萝卜素(Car)更为明显.群体叶片色素含量的敏感波段主要存在于可见光区,其中,红边区域表现显著.红边位置参数REPLE和REPIG与叶绿素关系较为密切,REPLE的表现较好.以REPLE为变量对Chla、Chlb和Chla+b进行方程拟合,决定系数R2分别为0.835、0.841和0.840;对Car含量进行方程拟合,其R2显著下降,且光谱参数间差异较小.经独立数据的检验表明,红边位置的估算结果较好,以REPIG为变量对Chlb进行预测,模型测试的R2和RE分别为0.632和18.2%;以REPLE为变量对Chla、Chla+b和Car含量进行预测,R2分别为0.805、0.744和0.588,RE分别为9.0%、9.7%和14.6%.表明红边位置与叶片色素含量关系密切且表现稳定,利用REPLE可以对小麦叶片Chla和Chla+b含量进行可靠的监测.

上海城市植被光谱反射特征分析

地物光谱特征是遥感机理的重要内容,也是遥感应用研究的重要依据。城市植被作为城市中重要的生命支持系统,对于维持城市生态系统平衡起着核心的作用。文章以上海城市植被为研究对象,利用美国ASD公司生产的FieldSpec3便携式地物波谱仪,通过对2009年10月金边黄杨,金丝桃,圆柏,麦冬,珊瑚树,小叶黄杨等野外和室内光谱数据采集和处理,获得了这6种城市植被的高光谱数据。采用Savitzky Go-lay平滑方法进行光谱噪声去除和利用微分光谱技术进行植被环境背景影响消除,分别从植被冠层光谱和叶片光谱以及微分光谱,对这6种城市植被的光谱特征以及光谱曲线的差异和变化规律进行分析。可为今后进一步研究城市植被理化性能、参数反演、植被分类、植被调查以及城市环境监测等遥感应用服务。

基于高光谱遥感的小麦籽粒产量预测模型研究

为了确立能够准确预测小麦籽粒产量的敏感光谱参数和定量模型,于2003-2006年连续3个生长季,通过不同小麦品种和不同施氮水平的4个大田试验,在小麦不同生育期采集田间冠层高光谱数据并测定植株氮含量、重量和叶面积指数及成熟期籽粒产量,定量分析小麦籽粒产量与冠层高光谱参数的相互关系。结果显示,小麦籽粒产量随施氮水平的提高而增加,不同地力水平间存在显著差异。灌浆前期叶片氮积累量和叶面积氮指数均能够较好地反映成熟期籽粒产量状况,而叶片氮含量和氮积累量及叶面积氮指数在拔节-成熟期的累积值与成熟期籽粒产量的回归拟合效果更好。对叶片氮含量和氮积累量及叶面积氮指数的光谱反演,在不同品种、氮素水平和年度间可以使用统一的光谱参数。根据“特征光谱参数-叶片氮素营养-籽粒产量”这一技术路径,以叶片氮素营养为交接点将模型链接,建立了基于灌浆前期高光谱参数及拔节期-成熟期特征光谱指数累积值的小麦籽粒产量预测模型。经两年独立试验数据检验表明,利用灌浆前期关键特征光谱指数可以有效地评价小麦成熟期籽粒产量状况,拔节-成熟期特征光谱指数的累积值能够稳定预报不同条件下小麦成熟期籽粒产量的变化。因此,利用冠层特征光谱指数可以快速无损地预报小麦成熟期籽粒产量。