那曲典型草地植被光谱特征分析

遥感是大尺度生态研究的重要工具之一,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。地面实测数据由于其高空间分辨率和高光谱分辨率,能够准确反映地物光谱信息,可以用来指导卫星遥感解译工作,同时为遥感监测草地退化、草地模型建立等提供数据支持。选取西藏那曲地区的优势植被类型作为研究对象,利用ASD FieldSpec 3便携式光谱仪测定优势种的冠层光谱并进行比较,并取其中一种优势种测量其在不同覆盖度和不同生长期的光谱反射特点。研究结果表明:①不同植被群落冠层光谱具有特殊的光谱曲线,可见光波段光谱反射率依次是紫花针茅、小嵩草和藏北嵩草,近红外波段光谱反射率则依次是小嵩草、藏北嵩草和紫花针茅;红边位置可以识别藏北嵩草,但是不能区分小嵩草和紫花针茅;②不同覆盖度的小嵩草红边、“绿峰”位置不随覆盖度的变化而发生变化;连续统去除后得到吸收深度随覆盖度的增加而变大,吸收峰面积随覆盖度的增加而增加;③小嵩草衰退期内,在可见光波段和红边波段,冠层光谱反射率随着叶绿素含量的减少而下降,出现“红边蓝移,绿峰下降”的现象。     

一种基于物理机理的高光谱遥感图像真彩色校正模型

针对高光谱遥感影像由于各波段光谱范围窄,难以获得符合人们视觉效果的真彩色合成影像问题,提出一种基于物理机理的高光谱遥感图像真彩色校正模型。该模型充分利用高光谱影像在红、绿、蓝反射区的所有谱段信息,通过插补波段并进行波段加权积分重建真彩色合成图像,进而结合实测地物反射率光谱,利用辐射传输模拟的方式,构建具备普适性的真彩色校正模型。利用航空高光谱遥感影像进行色彩校正实验的结果表明,所构建的真彩色校正模型能够很好地应用于高光谱遥感影像真彩色校正。     

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.     

森林树种高光谱波段的选择

高光谱是遥感技术发展的一个重要方向，也是地物识别的重要手段。本研究利用地物光谱仪对杉木、雪松、小叶樟树和桂花树4个树种进行高光谱数据测量，探索不同树种在不同波段上的识别能力。研究采用了逐步判别分析法和分层聚类法对实验数据进行数据分析。结果表明：逐步判别分析法选择的波段主要位于红、绿、蓝、和近红外区；分层聚类法选择的波段除了红、绿、蓝、和近红外波段外，还增加了蓝-绿边缘、绿-红边缘和红边区的波段。所选择的波段比原始波段在树种识别时具有更高的精度，最高识别精度达96．77％；边缘区波段对树种的识别有重要作用；用对数-微分变换处理较其他方法处理对树种识别有更好的效果。     

HSRS-SC:面向遥感场景分类的高光谱图像数据集

目的 场景分类是遥感领域一项重要的研究课题，但大都面向高分辨率遥感影像。高分辨率影像光谱信息少，故场景鉴别能力受限。而高光谱影像包含更丰富的光谱信息，具有强大的地物鉴别能力，但目前仍缺少针对场景级图像分类的高光谱数据集。为了给高光谱场景理解提供数据支撑，本文构建了面向场景分类的高光谱遥感图像数据集（hyperspectral remote sensing dataset for scene classification，HSRS-SC）。方法 HSRS-SC来自黑河生态水文遥感试验航空数据，是目前已知最大的高光谱场景分类数据集，经由定标系数校正、大气校正等处理形成。HSRS-SC分为5个类别，共1 385幅图像，且空间分辨率较高（1 m），波长范围广（380~1 050 nm），同时蕴含地物丰富的空间和光谱信息。结果 为提供基准结果，使用AlexNet、VGGNet-16、GoogLeNet在3种方案下组织实验。方案1仅利用可见光波段提取场景特征。方案2和方案3分别以加和、级联的形式融合可见光与近红外波段信息。结果表明有效利用高光谱影像不同波段信息有利于提高分类性能，最高分类精度达到93.20%。为进一步探索高光谱场景的优势，开展了图像全谱段场景分类实验。在两种训练样本下，高光谱场景相比RGB图像均取得较高的精度优势。结论 HSRS-SC可以反映详实的地物信息，能够为场景语义理解提供良好的数据支持。本文仅利用可见光和近红外部分波段信息，高光谱场景丰富的光谱信息尚未得到充分挖掘。后续可在HSRS-SC开展高光谱场景特征学习及分类研究。     

Spectral characteristics and feature selection of hyperspectral remote sensing data

Hyperspectral remote sensing data with bandwidth of nanometre (nm) level have tens or even several hundreds of channels and contain abundant spectral information. Different channels have their own properties and show the spectral characteristics of various objects in image. Rational feature selection from the varieties of channels is very important for effective analysis and information extraction of hyperspectral data. This paper, taking Shunyi region of Beijing as a study area, comprehensively analysed the spectral characteristics of hyperspectral data. On the basis of analysing the information quantity of bands, correlation between different bands, spectral absorption characteristics of objects and object separability in bands, a fundamental method of optimum band selection and feature extraction from hyperspectral remote sensing data was proposed.     

基于独立分量分析的高光谱遥感影像决策树分类

为解决高光谱遥感影像波段众多所带来的信息丰富与“维数灾难”间的矛盾并提高分类精度,针对传统特征选择方法信息损失大的缺陷,基于EO-1 Hyperion高光谱遥感影像,采用独立分量分析(ICA)和决策树分类(DTC)方法联合运作流程,开展影像的地物分类实验研究,提出了ICA-DTC模型。首先运用ICA方法对影像进行特征提取,并以所提取的独立分量特征及其他地理辅助要素组成分类指标集;继而选择适当的指标组合和阈值设定判别规则,建立DTC模型进行影像的地物分类;最后将分类结果与传统最大似然分类法进行比对。结果显示：从分类的总体精度看,前者可达89.34%,高出后者18.8%;从单一地物的分类精度看,前者仅水体的精度略低于后者,而其他11种地物的精度都高于后者。理论分析与实验结果均表明,ICA-DTC模型可有效提高复杂地形条件下的地物分类精度。     

Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra

Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5 μm–6 μm) and the thermal infrared (TIR; 8 μm–14 μm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5 μm–14 μm) results in reliable species discrimination.     

高光谱遥感在生态系统研究中的应用进展

遥感技术在生态系统调查与研究中具有广阔的应用潜力,但是传统的多光谱遥感主要侧重在面上的普查,很难对生态系统中各种复杂地物属性和生化参量进行精确反演。高光谱遥感突破了光谱分辨率的限制,大大提高了人们获取多种生态系统模型输入参数的类型和精度。在阐述高光谱遥感的原理和信息特点的基础上,系统评述了目前国内外高光谱遥感在生态系统过程与属性研究中的应用,并对未来高光谱遥感在生态学领域的研究方向做出展望。      

利用信息熵的高光谱遥感影像降维方法

高光谱遥感影像以其众多的波段数目，为地表观测提供近乎连续的波谱数据；然而海量的高光谱遥感影像存在着大量的信息冗余，为数据的处理带来了挑战。因此在对高光谱遥感影像进行存储、分析及可视化等操作之前，对高光谱遥感影像降维处理成为预处理的关键环节之一。利用信息熵理论，将高光谱遥感影像的各波段抽象为具有相关性的独立个体，设计了高光谱遥感影像的决策表矩阵，进而计算各波段的信息熵，量化各波段的信息量，从而将各波段根据信息增益进行排序。用户可根据高光谱遥感影像应用的精度需求，按排序选择波段组合，从而达到降维目的。以遥感分类结果的精度评价为例，对高光谱遥感降维方法的可行性和优越性进行评价。实验结果表明，该方法相较其他特征选取降维方法，能获得更高的分类精度。     

滨海盐土重金属含量高光谱遥感研究

高光谱遥感凭借其极高的光谱分辨率在获取有机质、矿物质等土壤组分定量信息的研究中表现出非凡的潜力。以如东县洋口镇为研究区,通过对土壤反射光谱的测量和同步的土壤化学分析,研究了土壤重金属Cr、Cu、Ni与土壤粘土矿物、铁锰氧化物以及碳酸盐之间的赋存关系。利用光谱一阶微分、倒数对数和连续统去除法对土壤光谱的处理,获得了土壤成分的特征波段,通过土壤重金属与土壤光谱变量的相关分析,并利用逐步回归分析方法,确立了3种重金属元素的最佳遥感模型。结果表明,研究区3种重金属与波长429 nm、470 nm、490 nm、1 430 nm、2 398 nm、2 455 nm处光谱变量具有很好的相关性,在所建立的逐步回归模型中,以一阶微分处理的模型精度最高。研究结果可以为高光谱遥感技术反演土壤重金属含量,进一步应用空间或航空遥感进行大尺度环境污染遥感、遥测信息提取和反演提供技术支撑。     

基于最大最小距离的高光谱遥感图像波段选择

为减少高光谱遥感图像光谱空间冗余,降低后续处理的计算复杂度,提出一种基于最大最小距离的高光谱图像波段选择算法。首先计算波段标准差,选定标准差最大的波段作为初始中心;然后使用最大最小距离算法得到相对距离较远的聚类中心,对波段进行聚类;最后使用K中心点算法更新聚类中心。实验仿真结果表明:通过基于最大最小距离算法选择的波段,能够选出同时满足信息量大、相关性小的要求的波段子集,并将获得的波段组合用于高光谱图像分类时,可以得到较好的分类精度。     

Absorption spectrum of phytoplankton pigments derived from hyperspectral remote-sensing reflectance

For a data set collected around Baja California with chlorophyll-a concentration ((chl-a)) ranging from 0.16 to 11.3 mg/m³, hyperspectral absorption spectra of phytoplankton pigments were independently inverted from hyperspectral remote-sensing reflectance using a newly developed ocean-color algorithm. The derived spectra were then compared with those measured from water samples using the filter-pad technique, and an average difference of 21.4% was obtained. These results demonstrate that the inversion algorithm worked quite well for the coastal waters observed and suggest a potential of using hyperspectral remote sensing to retrieve both chlorophyll-a and other accessory pigments.     

Hyperspectral Multiple Features Optimization Using Improved Firefly Algorithm

The utilization of hyperspectral remote sensing image is mainly based on the spectral information,and the spatial information is always be ignored.To solve this problem,a novel hyperspectral multiple features optimization approach based on improved firefly algorithm is presented.Firstly,four spatial features,the local statistical features,gray level co-occurrence matrix features,Gabor filtering features and morphological features of hyperspectral remote sensing image are extracted,and some spectral bands are selected and then combined with these spatial features,and the feature set is constructed.Then,the firefly algorithm is used to optimize the extracted features.In view of the slow convergence speed of firefly algorithm,we use the random inertia weight from particle swarm optimization algorithm to modifiy the location update formula of firefly algorithm,and JM(Jeffreys-Matusita)distance and Fisher Ratio are used as the objective function.Two urban hyperspectral datasets are used for performance evaluation,and the classification results derived from spectral information and spectral-spatial information are compared.The experiments show that random inertia weight can improve the speed of FA-based feature selection algorithm,the performance with multiple features is better than that of spectral information for urban land cover classification,The statistical results of the two sets of experimental data indicate that the selected number of morphological features are the most in the four spatial features.The local statistical features and morphological features are more helpful to the classification of hyperspectral remote sensing images than GLCM and Gabor features.     

Detecting photosynthetic algal pigments in natural populations using a high-spectral-resolution spectroradiometer

Reflectance data from a high spectral resolution spectroradiometer were obtained onboard a ship in Plymouth coastal waters. These data were analysed to detect algal photosynthetic accessory pigments for comparison with absorption spectra as measured in the laboratory by a spectrophotometer. The overall spectral characteristics of Plymouth waters allowed identification as to population composition. Derivative analysis of the spectra was used to resolve characteristic peaks of specific pigments. It was determined that chlorophyll pigments, a specific carotenoid and sea water absorption bands were detectable in the reflectance data. Absorption bands of photosynthetic and accessory pigments were assessed through chromatographic pigment analysis.     

波段排序的高光谱影像3维混合树编码方法

目的 高光谱影像压缩的关键技术是对空间维和光谱维的去相关性。根据高光谱影像数据结构的特点,如何有效去除其空间相关性与谱间相关性是高光谱影像压缩中至关重要的问题。对高光谱影像进行编码时,3维小波变换是极为有效的去除冗余的方法。因此提出了一种通过波段排序并结合3维混合树型结构对高光谱影像3维小波变换系数进行编码的算法。方法 首先,将高光谱影像按照自然波段顺序进行波段分组,并对每组影像进行相邻影像的谱间相关性统计;其次,对相关性较弱的波段组,建立以影像波段序号为顶点、影像相关性系数为边的完全图,对这个完全图求其最大汉密尔顿回路。按照求得的最大汉密尔顿回路顺序对该波段组进行重新排序,从而提高波段组的谱间相关性;在此基础上,对重新排序后的波段组进行3维小波变换,并通过3维混合树结构对3维小波变换系数进行零树编码。结果 通过对大量AVIRIS型高光谱影像数据的仿真实验,验证了本文方法的有效性。对相关性较低的波段组,加入排序算法后,其解码影像与未排序时比,峰值信噪比有了一定的提高。通过实验统计,算法平均用时2.7579s。结论 由于采用了对弱相关性波段组的重新排序机制,使得基于混合树结构的3维零树编码出现了更多有效的零树,在一定程度上提高了编码效率。通过实验统计算法用时,表明该方法以较小的时间代价获得了解码效果的提升。     

A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

This review paper evaluates the potential of hyperspectral remote sensing for assessing species diversity in homogeneous (non-tropical) and heterogeneous (tropical) forest, an increasingly urgent task. Existing studies of species distribution patterns using hyperspectral remote sensing have used different techniques to discriminate different species, in which the wavelet transforms, derivative analysis and red edge positions are the most important of them. The wavelet transform is used based on its effectiveness and determined as the most powerful technique to identify species. Furthermore, estimations of relationships between spectral values and species distributions using chemical composition of foliage, tree phenology, selection of signature training sites based on field measured canopy composition, selection of the best wavelet coefficient and waveband regions may be useful to identify different plant species. This paper presents a summary on the feasibility, operational applications and possible strategies of hyperspectral remote sensing in forestry, especially in assessing its biodiversity. The paper also reviews the processing and analysis of techniques for hyperspectral data in discriminating different forest tree species.     

基于无人机高光谱影像的三江源草种精细识别研究

草种精细识别对三江源区草地生态系统退化监测具有重要意义。基于无人机高光谱遥感系统,获取三江源草地退化典型区的高光谱影像。在对原始光谱特征利用XGBoost进行优化选择的基础上,结合扩展形态学属性剖面特征,利用稀疏多项式逻辑回归与自适应稀疏表示两种分类方法分别对影像上的不同可食与毒杂草种进行精细识别,在此基础上提出形状自适应的后处理方法对识别结果进行平滑处理。结果表明：①利用XGBoost方法选择出重要性高的光谱特征能提升高光谱数据的识别效果并节省运行时间;②利用空间—光谱特征的识别方法相较于仅利用光谱特征的方法可以有效改善草种识别效果,使总体精度提升4%~5%;③利用两种稀疏表示方法在小样本的情况下对草种精细识别的精度分别达到94.07%、93.15%,利用形状自适应后处理方法能有效提高多种毒杂草种的识别精度,使得总体精度分别提升约1.64%和1.12%。基于特征挖掘的稀疏表示分类方法能实现高精度的无人机高光谱影像草种精细识别,为更大范围的草原物种精细识别提供了技术支撑。     

地球观测1号高光谱与全色图像融合的最佳方法

受制于成像原理及制造技术等因素,航天高光谱遥感图像的空间分辨率相对较低,为此提出将高光谱图像与高空间分辨率图像进行融合处理,设计最佳的增强高光谱遥感图像空间分辨率的融合算法。针对地球观测1号(EO-1)Hyperion高光谱图像和高级陆地成像仪（ALI）全色波段图像的特点,从9种具体遥感图像融合算法中选用4种融合算法开展山区与城市的数据融合实验,即Gram-Schmidt光谱锐化融合法、平滑调节滤波(SFIM)变换融合法、加权平均法（WAM）融合法和小波变换（WT）融合法,并分别从定性、定量和分类精度三方面对这些方法的融合效果进行综合评价与对比分析,从而确定适合EO-1高光谱与全色图像融合的最佳方法。实验结果显示：从图像融合效果看,在所采用的4种融合方法中,Gram-Schmidt光谱锐化融合法的效果最好;从图像分类效果看,基于融合图像的分类效果要优于基于源图像的分类效果。理论分析与实验结果均表明：Gram-Schmidt光谱锐化融合法是一种较为理想的高光谱与高空间分辨率遥感图像的融合算法,为提高高光谱遥感图像的清晰度、可靠性及图像的地物识别和分类的准确性提供有力的支持。     

高光谱数据的光谱信息特点及面向对象的特征参数选择——以北京顺义区为例

波段宽度为纳米级的高光谱数据,具有几十乃至几百个光谱通道,它们各有不同的特点。如何根据具体的应用目的,在这众多的波段中选择出最佳波段和特征参数,对于有效地进行高光谱数据的处理、分析及信息提取至关重要。以北京顺义区高光谱数据为例,首先分析了通道间的相关性,根据通道的相关性大小和分组块状结构特点,将其分为若干组;然后全面分析了高光谱数据的光谱信息特征,在综合考虑各波段的信息含量、波段间的相关性以及地物光谱的吸收特性和可分性等因素
的基础上,提出了面向对象的分层多次选择高光谱数据最佳波段和提取特征参数的基本思路和方法;最后用其它地区的成像光谱数据对此方法进行了验证。