Discrimination of arid vegetation with airborne multispectralscanner hyperspectral imagery

Hyperspectral imagery from the airborne multispectral scanner was evaluated for discrimination and mapping of vegetation components in a semi-arid rangeland environment in Southern Australia. Automated unmixing of two image strips with 5-m resolution revealed several vegetation endmembers in the visible, near infrared (near-IR), and short-wave infrared portions of the imagery. Identity of the endmembers was determined through examination of their short-wave infrared and full-wavelength spectra, and their mapped distributions and correlation with percent cover of vegetation species were measured in sample plots. In addition, to assist interpretation of the image signatures, short-wave infrared reflectance spectra for the dominant vegetation components at the study site were collected with a portable infrared mineral analyzer (PIMA) spectrometer. Endmembers separately mapped included Eucalyptus and other trees such as sugarwood, understorey chenopod shrubs, dry plant litter, and soil surface cryptogamic crust. Several endmembers were significantly positively correlated with field measurements of plant cover. Most of the tree canopy endmembers showed broad cellulose-lignin absorption features in the short-wave infrared (SWIR), and narrower absorptions caused by plant waxes and oils. The field spectra confirmed that the sclerophyll and xerophytic plants show identifiable cellulose, lignin, and plant wax absorption features, even when live and actively photosynthesising. This spectral expression of biochemical constituents in live plants points to the benefit of using the whole spectral range from visible to short-wave infrared in vegetation studies     

Scaling-up and model inversion methods with narrowband opticalindices for chlorophyll content estimation in closed forest canopieswith hyperspectral data

Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R₇₅₀/R₇₁₀ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized     

森林冠层氮含量遥感估算

使用高光谱数据估算叶片与冠层尺度的森林氮含量.首先采用基于高斯误差函数的BP神经网络Erf-BP建立叶片尺度氮含量的遥感估算模型;其次根据几何光学模型原理,推导冠层光谱与叶片光谱的尺度转化函数,将Hy-perion影像的冠层光谱转换到叶片尺度并反演叶片尺度的氮含量;最后,利用森林结构参数LAI得到研究区域冠层尺度氮含量.结果表明,隐含层包含8个神经元的Erf-BP模型最优,检验精度为76.8597%;利用尺度转化函数估算670 nm和865 nm冠层光谱与实测光谱决定系数为0.5203和0.4117;反演叶片尺度氮含量与实测数据的决定系数为0.7019;该方法为高精度快速估算叶片和冠层尺度森林氮含量提供参考.     

Remote sensing the biochemical composition of a slash pine canopy

Airborne imaging spectrometers can record spatially-explicit information on the absorption features associated with foliar biochemicals in a forest canopy. The spectra of a single species pine canopy were recorded by the National Aeronautics and Space Administration's (NASA) Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Up to three wavebands were correlated to the concentration of chlorophyll, nitrogen, lignin, and cellulose (R²=0.96,0.94,0.93, and 0.61, respectively) and the content of these four biochemicals (R²=0.98,0.91,0.88, and 0.92, respectively). The AVIRIS data were used, for the first time, to map the content of these biochemicals within the forest canopy and the accuracy was between 3-7% of the mean     

Invertible canopy reflectance modeling of vegetation structure insemiarid woodland

The Li-Strahler canopy reflectance model, driven by Landsat Thematic Mapper (TM) data, provided regional estimates of tree size and density in two bioclimatic zones in West Africa. This model exploits tree geometry in an inversion technique to predict average tree size and density from reflectance data using a few simple parameters measured in the field (spatial pattern, shape, and size distribution of trees) and in the imagery (spectral signatures of scene components). The model was tested in sparse woodland and wooded grassland in the Sahelian and Sudanian bioclimatic zones in West Africa     

利用背景残差数据检测高光谱图像异常

针对高光谱图像微小目标检测中存在的严重背景干扰问题,提出了一种基于背景残差数据的非线性异常检测算法.首先利用提取的背景光谱端元对图像各像元进行光谱解混,实现了目标信息和复杂背景信息的分离;接着将含有丰富目标信息的解混残差数据非线性映射到高维特征空间,可以充分挖掘高光谱图像波段间隐含的非线性信息,并在特征空间利用RX算子完成目标的检测,从而在抑制大概率背景信息的基础上有效地利用了高光谱图像波段间的非线性统计特性.为了验证算法的有效性,利用真实的AVIRIS数据进行了实验研究,并与经典RX算法、未抑制背景的特征空间核RX算法的检测结果相比较,结果表明基于背景残差数据的检测算法具有良好的检测性能和较低的虚警,且运算复杂度较低.     

Measuring trace gases in plumes from hyperspectral remotely sensed data

A method [joint reflectance and gas estimator (JRGE)] is developed to estimate a set of atmospheric gas concentrations in an unknown surface reflectance context from hyperspectral images. It is applicable for clear atmospheres without any aerosol in a spectral range between approximately 800 and 2500 nm. Standard gas by gas methods yield a 6% rms error in H/sub 2/O retrieval from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, reaching several tens percent for a set of widespread ground materials and resulting from an simplifying assumption of linear variations of the reflectance model within gas absorption bands and partial accounting of the gas induced signal. JRGE offers a theoretical framework consisting in a two steps algorithm that accounts for sensor characteristics, assumptions on gas concentrations and reflectance variations. It estimates variations in gas concentrations relatively to a standard atmosphere model. An adaptive cubic smoothing spline like estimation of the reflectance is first performed. Concentrations of several gaseous species are then simultaneously retrieved using a nonlinear procedure based on radiative transfer calculations. Applied to AVIRIS spectra simulated from reflectance databases and sensor characteristics, JRGE reduces the errors in H/sub 2/O retrieval to 2.87%. For an AVIRIS image acquired over the Quinault prescribed fire, far field CO/sub 2/ estimate (348 ppm, about 6% to 7% rms) is in agreement with in situ measurement (345-350 ppm) and aerosols yield an underestimation of total atmospheric CO/sub 2/ content equal to 5.35% about 2 km downwind the fire. JRGE smoothes and interpolates the reflectance for gas estimation but also provides nonsmoothed reflectance spectra. JRGE is shown to preserve various mineral absorption features included in the AVIRIS image of Cuprite Mining District test site.     

A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data

Linear spectral unmixing is a commonly accepted approach to mixed-pixel classification in hyperspectral imagery. This approach involves two steps. First, to find spectrally unique signatures of pure ground components, usually known as endmembers, and, second, to express mixed pixels as linear combinations of endmember materials. Over the past years, several algorithms have been developed for autonomous and supervised endmember extraction from hyperspectral data. Due to a lack of commonly accepted data and quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared by using a unified scheme. In this paper, we present a comparative study of standard endmember extraction algorithms using a custom-designed quantitative and comparative framework that involves both the spectral and spatial information. The algorithms considered in this study represent substantially different design choices. A database formed by simulated and real hyperspectral data collected by the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) is used to investigate the impact of noise, mixture complexity, and use of radiance/reflectance data on algorithm performance. The results obtained indicate that endmember selection and subsequent mixed-pixel interpretation by a linear mixture model are more successful when methods combining spatial and spectral information are applied.     

Quantitative Mineral Assessment of Apatite, Calcite/Dolomite, and Phlogopite Powder Mixtures by Using VSWIR Reflectance

Quantitative assessment of mineral powders made from mono/two-mineral blocks sampled from the Siilinjarvi apatite ore deposit is the target of this paper. The blocks were composed of apatite, calcite/dolomite, and phlogopite. The blocks were powdered and mixed into a set of 21 well-documented mixtures. Their visible to short-wave infrared (VSWIR) reflectance spectra were recorded by using the FieldSpecFR Spectrometer. Linear unconstrained unmixing of the spectra resulted in highly biased estimates in spite of the fact that the mean absolute residual error of the unmixing was very small. The bias, which is actually induced by camouflage between mineral classes, could approximately be removed by a polynomial function. This resulted in unbiased estimates for apatite calcite/dolomite and phlogopite. Best results were obtained when the wavelengths were limited to the SWIR area only. This two-part procedure consisting of the linear unmixing and the camouflage correction gives one more aspect to subpixel classification. Training data are given not only for the classes but also for their interaction. In this paper, the mean absolute residual error of the estimated volumetric fractions of apatite, calcite/dolomite, and phlogopite from the spectra was 1.32%, which is smaller than the result in the previous VSWIR studies concerning the assessment of mineral powders. This reflectance method offers a very quick tool for several mineral-related applications.     

多角度成像解析大豆冠层的二向反射特征

植被冠层二向性反射特征是定量遥感必须关注的一个问题。论文借助自主研发的多角度成像系统,在不同观测时间对不同种植密度下的大豆冠层进行多角度成像数据采集,通过对图谱合一的高光谱影像中大豆植株、土壤背景和阴影叶片进行逐步分离,对比分析纯大豆植被与植被-土壤混合冠层的二向反射（Bidirectional Reflectance, BR）变化特征,研究发现:在主平面观测时,土壤光谱去除后,即纯植被冠层反射率在前向观测时,随着天顶角的减小而增大,这不同于植被和土壤同时存在时的研究结果（BR 随着天顶角的增加而增大）;当观测方向由主平面的前向朝后向变动时,可见光和近红外波段的纯植被冠层反射率表现为逐步增大的趋势,这和土壤光谱去除前的变化趋势也不同;在垂直主平面观测时,去除土壤背景后的纯植被冠层反射率与混合植被反射率特征有相同的趋势,但在垂直主平面方向的对称性更强。上述结果在不同密度、不同观测时间的大豆冠层BR 特征有相近的趋势,这为多角度遥感的发展提供了必要的基础研究。     

高光谱解混方法研究

高光谱图像的空间分辨率较低,导致大量混合像元存在于高光谱图像中。混合像元的存在是使高光谱图像目标分类准确率降低的主要原因之一。高光谱像元解混在高光谱遥感图像处理中具有非常重要的意义。高光谱像元解混主要分为线性和非线性光谱解混两种方法,研究最广泛的是线性光谱解混。归纳了线性光谱解混的两个步骤:（1）提取纯净像元中地物的光谱信号,即提取端元,这是关键步骤;（2）利用端元的加权线性组合对混合像元进行光谱解混,即丰度反演。简述了端元提取及丰度反演研究的主要进展,介绍了端元提取的几种典型算法。通过归纳、对比和分析,总结了不同端元提取方法的特点,并对高光谱解混的研究前景进行了展望。     

一种新的基于Fisher判别的混合像元分解算法:室内控制实验结果分析

混合像元分解技术(Spectral Mixture Analysis, SMA)是遥感图像处理的重要手段之一.传统方法假设每个端元具有稳定的光谱特征,然而端元内光谱差异普遍存在,这将导致混合像元分解精度的降低.针对该问题,提出了基于Fisher判别 (Fisher Discriminant Analysis, FDA) 的混合像元分解算法.Fisher判别对光谱各波段进行线性组合,使得转换后的光谱值分离度最大,即端元内的光谱差异较小而端元间的光谱差异较大.利用转换后的光谱对混合像元进行分解可以最大程度地减少端元内光谱差异对分解结果的影响.利用该方法对室内控制实验的模拟混合像元光谱进行分解,并与过去提出的几种混合像元分解技术进行比较,结果显示新方法相比传统方法在分解精度上有相当程度的提高.     

Spatial/spectral endmember extraction by multidimensional morphological operations

Spectral mixture analysis provides an efficient mechanism for the interpretation and classification of remotely sensed multidimensional imagery. It aims to identify a set of reference signatures (also known as endmembers) that can be used to model the reflectance spectrum at each pixel of the original image. Thus, the modeling is carried out as a linear combination of a finite number of ground components. Although spectral mixture models have proved to be appropriate for the purpose of large hyperspectral dataset subpixel analysis, few methods are available in the literature for the extraction of appropriate endmembers in spectral unmixing. Most approaches have been designed from a spectroscopic viewpoint and, thus, tend to neglect the existing spatial correlation between pixels. This paper presents a new automated method that performs unsupervised pixel purity determination and endmember extraction from multidimensional datasets; this is achieved by using both spatial and spectral information in a combined manner. The method is based on mathematical morphology, a classic image processing technique that can be applied to the spectral domain while being able to keep its spatial characteristics. The proposed methodology is evaluated through a specifically designed framework that uses both simulated and real hyperspectral data.     

一种考虑光谱变异性的高光谱图像非线性解混算法

非线性解混可以解释高光谱图像复杂场景中的非线性混合效应,但地物的光谱变异性是其中的一个难点。提出一种考虑光谱变异性的无监督非线性解混算法。通过核函数将原始高光谱图像数据隐式地映射到高维特征空间中,从而在该空间中结合光谱变异性进行线性解混;与此同时,依据实际地物的分布特性,添加丰度和光谱变异系数的局部平滑约束。模拟和真实高光谱数据的实验结果表明,该方法能克服不同非线性混合场景中存在的光谱变异性问题,提高光谱解混的精度。     

基于背景残差数据的高光谱图像异常检测算法

针对高光谱图像微小目标检测中存在的严重背景干扰问题,提出了一种基于背景残差数据的非线性异常检测算法.首先利用提取的背景光谱端元对图像各像元进行光谱解混,实现了目标信息和复杂背景信息的分离;接着将含有丰富目标信息的解混残差数据非线性映射到高维特征空间,可以充分挖掘高光谱图像波段间隐含的非线性信息,并在特征空间利用RX算子完成目标的检测,从而在抑制大概率背景信息的基础上有效地利用了高光谱图像波段间的非线性统计特性.为了验证算法的有效性,利用真实的AVIRIS数据进行了实验研究,并与经典RX算法、未抑制背景的特征空间核RX算法的检测结果相比较,结果表明基于背景残差数据的检测算法具有良好的检测性能和较低的虚警,且运算复杂度较低.     

Improved estimation of fraction images using partial imagerestoration

It is noted that mixed pixels are unavoidable in remote sensing imagery, but signature mixing models can be effective in improving the usefulness of the image data. Unmixing error caused by sensor system point spread function (PSF) degradation is studied, and the application of image restoration before spectral unmixing to improve the unmixing results is investigated. It is shown that the characteristics of the problem makes the design of a restoration filter differ from that for visual enhancement. Simulation results show that, depending on the scene complexity, unmixing error can be reduced by up to 72% using the proposed restoration scheme. It is concluded that image restoration is useful in increasing the accuracy of spectral unmixing     

Solar zenith angle effects on forest canopy hemisphericalreflectances calculated with a geometric-optical bidirectionalreflectance model

The bidirectional reflectance distribution function (BRDF) provided by the Li-Strahler geometric-optical forest canopy model has been integrated to provide spectral instantaneous hemispherical reflectances of sparsely vegetated surfaces. Further integration over the Sun's zenith angles can yield daily or longer interval hemispherical reflectances as well. A variety of simulated canopies were modeled with varying solar angles. In all cases, as the geometric-optical model introduced increased shadowing of the surface with increased solar zenith angle, the direct-beam hemispherical surface reflectance gradually decreased. The hemispherical reflectance values are direct beam calculations and do not directly include canopy multiple scattering and leaf specularity or consider the impact of diffuse irradiance. These limitations are acceptable for sparse canopies, in which 3D shadowing effects are large. However, radiative transfer calculations have shown that these phenomena must be incorporated before truly realistic modeling of hemispherical surface reflectances can be achieved for dense canopies     

A novel approach for spectral unmixing, classification, and concentration estimation of chemical and biological agents

In this paper, spectral unmixing methods, which are extensively used in hyperspectral imaging area, are proposed for classification and abundance fraction (concentration) estimation of chemical and biological agents that exist in the mixture form. Several government-furnished datasets, which were collected through the infrared spectrum method, were thoroughly analyzed. Two similarity measures-the spectral angle mapper and spectral information divergence-were investigated in order to provide a quantitative comparison basis with respect to the performance of the applied spectral unmixing methods in the existence of similar and distinct agents. The use of the similarity measures provided valuable information about the signature characteristics of the agents, which led to a better understanding about the capabilities of the investigated methods. The orthogonal subspace projection (OSP) method was investigated as the first unmixing, classification, and abundance estimation technique. It was observed that the OSP method provided good results when the number of agents in the database was small and was composed of distinct agents. However, when the number of agents was incremented by adding agents that share similar characteristics, the abundance estimation accuracy gradually degraded in addition to generating negative abundance fraction estimates. The second investigated unmixing method was called nonnegatively constrained least squares (NCLS). The results and analyses indicated that the NCLS method outperformed the OSP approach by providing considerably more accurate fraction estimates while at the same time not generating any negative fraction estimates; thus, the use of the NCLS method was found to be promising in detection and abundance fraction estimation of chemical and biological agents that exist in the form of mixtures. In addition, efficient implementation of NCLS has resulted in much lower computations than the conventional OSP implementation.     

An algorithm using visible and 1.38-/spl mu/m channels to retrieve cirrus cloud reflectances from aircraft and satellite data

The Moderate Resolution Imaging Spectro-Radiometer (MODIS) on the Terra spacecraft has a channel near 1.38 /spl mu/m for remote sensing of high clouds from space. The implementation of this channel on MODIS was primarily based on previous analysis of hyperspectral imaging data collected with the Airborne Visible Infrared Imaging Spectrometer (AVIRIS). We describe an algorithm to retrieve cirrus bidirectional reflectance using channels near 0.66 and 1.38 /spl mu/m. It is shown that the apparent reflectance of the 1.38-/spl mu/m channel is essentially the bidirectional reflectance of cirrus clouds attenuated by the absorption of water vapor above cirrus clouds. A practical algorithm based on the scatterplot of 1.38-/spl mu/m channel apparent reflectance versus 0.66-/spl mu/m channel apparent reflectance has been developed to scale the effect of water vapor absorption so that the true cirrus reflectance in the visible spectral region can be obtained. To illustrate the applicability of the present algorithm, results for cirrus reflectance retrievals from AVIRIS and MODIS data are shown. The derived cirrus reflectance in the spectral region of 0.4-1 /spl mu/m can be used to remove cirrus contamination in a satellite image obtained at a visible channel. An example of such an application is shown. The spatially averaged cirrus reflectances derived from MODIS data can be used to establish global cirrus climatology, as is demonstrated by a sample global cirrus reflectance image.