基于支持向量回归的光谱反射率重建方法

提出了一种基于支持向量回归和小框架核的光谱反射率重建方法。光谱反射率重建是光学研究的一个重要问题,其目的是通过各种成像设备所获取的与设备相关的RGB三色值重建出物体本身固有的与设备和光照都无关的光谱反射率。回归方法已经在这一领域取得了广泛应用,如基于多项式模型的正则化最小二乘方法、基于核的正则化最小二乘方法等。提出了一种新的光谱反射率重建方法,这种方法采用了一种可以减弱样本不规则噪音影响的小框架核函数,并将其用于支持向量回归来重建光谱反射率函数。实验表明,新方法可以提高光谱反射率重建的精度和稳定性。     

基于反射光谱数据的茶树叶片SPAD值估算模型研究

实验中将茶树的叶片分为3个部位进行研究,每个部位各采集50个叶片,30组数据作为预测样本,20组数据作为试验模拟样本。设计绿峰位置、绿峰最大反射率、红谷位置、红谷最小反射率、红边位置、红边对应的最大一阶导数反射率、绿峰对应的最大反射率和红谷对应的最小反射率的比值指数以及它们的归一化指数等8个反射光谱参数。首先对茶树叶片的反射光谱参数和茶树叶片的SPAD值进行相关分析;其次以8个反射光谱参数作为自变量,茶树叶片的SPAD值为因变量,进行逐步回归分析,确定茶树不同部位叶片的回归方程.茶树A部位嫩叶片的SPAD值预测模型以λr、Rg/Ro为自变量,其模拟的调整决定系数为0.461;茶树B部位的成熟叶子的SPAD值预测模型以Rg、Rg/Ro、Rg-Ro/Rg+Ro为自变量,其模拟的调整决定系数为0.882;茶树C部位的老叶子的SPAD值预测模型以λr、Dr为自变量,其模拟的调整决定系数为0.407。结果表明,利用反射高光谱参数预测茶树不同部位叶片的SPAD模型是成功的。     

Accurately estimating reflectance parameters for color and gloss reproduction

A new method is described to estimate diffuse and specular reflectance parameters using spectral images, which overcomes the dynamic range limitation of imaging devices. After eliminating the influences of illumination and camera on spectral images, reflection values are initially assumed as diffuse-only reflection components, and subjected to the least squares method to estimate diffuse reflectance parameters at each wavelength on each single surface particle. Based on the dichromatic reflection model, specular reflection components are obtained, and then subjected to the least squares method to estimate specular reflectance parameters for gloss intensity and surface roughness. Experiments were carried out using both simulation data and measured spectral images. Our results demonstrate that this method is capable of estimating diffuse and specular reflectance parameters precisely for color and gloss reproduction, without requiring preprocesses such as image segmentation and synthesis of high dynamic range images.     

Estimating albedo from limited spectral and angular data

A database of synthetic albedo and directional reflectance values for vegetated surfaces was constructed utilizing mathematical models. This database enables the comparison of albedo with reflectances measured in narrow spectral bands in particular viewing directions for specified vegetation canopy and solar conditions. The analysis reported here is for spectral bands and angular regimes corresponding to the Along-Track Scanning Radiometer (ATSR-2) sensor on ERS-2. In the analysis multiple linear regression is used to calculate the best fit between modelled reflectance and modelled albedo. A primary estimate of albedo is calculated using reflectance data from the nadir direction only. Data from the forward view of the ATSR sensor are then used to provide additional information to correct the nadir estimate. The relationship between the regressed coefficients and the illumination conditions was investigated in order to provide a universal albedo estimation. Preliminary results for representative solar zenith and azimuth angles show an extremely good fit between modelled albedo and that estimated using the modelled ATSR-2 reflectance.     

Examples of high resolution visible to near-infrared reflectance spectra and a standardized collection for remote sensing studies

Abstract. A number of sets of high resolution (001 μm or better) spectra are described which span the major part to the visible short-wave infrared spectrum (0.4-2.5μm). These data sets include soils, both wet and dry, conventional agriculture, grasses, and shrubs, igneous and sedimentary rocks, minerals, and a variety of fabrics, building materials, and metals. Most are from the laboratory, some are field measurements. These collections do not include bi-directional reflectance data sets, nor limited domain (～0.4-l.0μm) visible near-infrared spectral data sets. To facilitate intercomparisons and spectral matching with remotely-sensed data such as the aircraft instrument (AVIRIS) a standardized collection is described in which all spectra are presented at 0.01μm intervals, in a common format. This collection is available for research studies on a PC diskette.     

Variability of high-resolution crop reflectance spectra

Abstract

High-spectral-resolution reflectance spectra from ground and helicopter measurements of agricultural crops and soils were analyzed to determine spectral variability in the visible and near-infrared (0 4-2 38 μm), using a procedure previously applied to thermal infrared emittance spectra of the atmosphere and to reflectance spectra of soil samples. Five spectral basis functions were sufficient to describe separately the ground and helicopter data, six or, at most, seven are sufficient to describe the pooled data. Thus, five to seven relatively broad band measurements, together with basis functions developed in this analysis, are sufficient to describe the variability of both data sets, to within differences that are probably associated with the measurement process and instrument noise.     

A self-consistent semi-empirical absorption correction technique

A method of calculating azimuthal scans for subsequent correction of X-ray diffractometer intensity data for absorption is described. The technique is based on the premise that systematic differences observed in the intensities of symmetry equivalent reflections (measured during data collection) are due to the relative transmission factors as a function of the primary and secondary beam path directions. Azimuthal scans are calculated from a least squares fit to these equivalent reflections; these scans may then be applied to the data to correct for absorption according to a simple geometric model.     

Computing LTS Regression for Large Data Sets

Data mining aims to extract previously unknown patterns or substructures from large databases. In statistics, this is what methods of robust estimation and outlier detection were constructed for, see e.g. Rousseeuw and Leroy (1987). Here we will focus on least trimmed squares (LTS) regression, which is based on the subset of h cases (out of n) whose least squares fit possesses the smallest sum of squared residuals. The coverage h may be set between n/2 and n. The computation time of existing LTS algorithms grows too much with the size of the data set, precluding their use for data mining. In this paper we develop a new algorithm called FAST-LTS. The basic ideas are an inequality involving order statistics and sums of squared residuals, and techniques which we call ‘selective iteration’ and ‘nested extensions’. We also use an intercept adjustment technique to improve the precision. For small data sets FAST-LTS typically finds the exact LTS, whereas for larger data sets it gives more accurate results than existing algorithms for LTS and is faster by orders of magnitude. This allows us to apply FAST-LTS to large databases.     

中医面诊中面部肤色特征基函数确定

为满足中医面诊中对面色精确描述的要求,用光谱反射率来表述颜色.基于有限维模型,面部的光谱反射率可以用一组基函数的线性组合表示.因此可采用少量的图像通道数据求解像素位置基函数系数,复原面部各像素点的光谱反射率.而基函数的确定是光谱反射率复原的基础,针对面部肤色的特点,基于标准色系及真实肤色研究了不同面部光谱反射率样本集条件下,确定的面部光谱反射率基函数对复原精度的影响,并确定了适用于面部光谱反射率复原的优选样本集及基函数组合.结果表明,优选样本集使用较少的基函数就可以达到良好的复原效果,将为基于中医面诊光谱反射率复原提供依据,进而促进中医面诊的客观化发展.     

An improved finite element formulation derived from the method of weighted residuals

By replacing the residual with its least squares fit an improvement has been made to the method of weighted residuals. For finite element applications the least squares fit is made on an element basis. The improved formulation is illustrated by a number of examples drawn mainly from fluid flow problems. The new formulation is closely related to the use of reduced integration, and detailed comparisons with exact and reduced integration are presented.     

A computational strategy exploiting genetic algorithms to recover color surface reflectance functions

Information about the spectral reflectance of a color surface is useful in many applications. Assuming that reflectance functions can be adequately approximated by a linear combination of a small number of basis functions, we address here the recovery of a surface reflectance function, given the tristimulus values under one or more illuminants. Basis functions presenting different characteristics and cardinalities are investigated, and genetic algorithms are employed to optimize the estimation. Our analysis of a variety of standard datasets provides information about the ability of each set of basis functions we used to model generic reflectance spectra.     

Optimization approach to dichromatic images

A method for recovering the spectrum of both the interface and the body reflectance for images composed of dichromatic surfaces is presented. An important assumption is that the spectrum of the interface component is the same as that of the illuminant. It is also assumed that the image is presegmented into dichromatic patches, that surfaces possess specularities, and that these highlights change geometrically differently from the shading. The method is based on minimizing the sum of squares of deviations from the dichromatic model over all the patches in the image, by using finite-dimensional linear models to approximate spectral functions. We point out shortcomings in the accuracy of such models when specularities are present in images. Results are presented for synthesized images made up of shaded patches with highlights. It is shown that the method does reasonably well in recovering interface and body colors as well as the illuminant spectrum and body spectral reflectance function.This work was supported in part by grants from the Centre for Systems Science of Simon Fraser University.     

Modeling multivariate time series on manifolds with skew radial basis functions

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.     

Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran

The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR-SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, MgOH spectral features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and AlOH. The laboratory spectral emittance spectra also revealed a wide wavelength range of SiO spectral features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR + SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR + SWIR data and TIR data alone.     

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.     

Satellite detection of ice melt over the Arctic Ocean

The relationships between spectral reflectance and plot variables in a desert shrub community were studied by repeated observation as biomass was removed from 2·93m² plots. As a result of these repeated observations, interdependence effects were present in the data sets. The spectral/plot relationships were discovered to be weaker with the removal of this observational interdependence. Middle infrared reflectance (2·08–2·35μm) was found to account for more of the variation in the group of plot variables than did reflectance in the other spectral regions tested, and the variable of total moisture was best for explaining the variability of the spectral data.     

Interpretation of airborne spectral reflectance measurements over Georgian Bay

Airborne spectral measurements were made along 3 flight lines over Georgian Bay in August and October 1974. Spectral data were collected in the wavelength regions 0.44 to 0.46 μm, 0.55 to 0.59 μm, 0.68 to 0.70 μm and 0.72 to 0.75 μm using a four-channel spectral scanning photometer. The airborne spectral reflectance curves over several stations were compared with subsurface volume reflectance measurements made as part of the Canada Centre for Inland Waters 1974 Lake Huron-Georgian Bay ship cruise. These comparisons permitted an evaluation of the water surface reflection effects on the airborne data. The airborne spectral reflectance curves are then interpreted using a two-flow radiative transfer model to derive chlorophyll concentration line profiles. Correlation coefficients between calculated and ship-measured chlorophyll data range from 0.810 to 0.984.     

Semiparametrically weighted robust estimation of regression models

A class of two-step robust regression estimators that achieve a high relative efficiency for data from light-tailed, heavy-tailed, and contaminated distributions irrespective of the sample size is proposed and studied. In particular, the least weighted squares (LWS) estimator is combined with data-adaptive weights, which are determined from the empirical distribution or quantile functions of regression residuals obtained from an initial robust fit. Just like many existing two-step robust methods, the LWS estimator with the proposed weights preserves robust properties of the initial robust estimate. However, contrary to the existing methods and despite the data-dependent weights, the first-order asymptotic behavior of LWS is fully independent of the initial estimate under mild conditions. Moreover, the proposed estimation method is asymptotically efficient if errors are normally distributed. A simulation study documents these theoretical properties in finite samples; in particular, the relative efficiency of LWS with the proposed weighting schemes can reach 85%-100% in samples of several tens of observations under various distributional models.     

Data‐based design of robust fault detection and isolation residuals via LASSO optimization and Bayesian filtering

In this paper, a data‐based approach for the design of structured residual subsets for the robust isolation of sensor faults is proposed. Linear regression models are employed to estimate faulty signals and to build a set of primary residuals. L₁‐regularized least squares estimation is used to identify model parameters and to enforce sparsity of the solutions by increasing the regularization weight. In this way, it is possible to generate a set of residuals generators with different fault sensitivity. Then, a residual selection procedure based on fault sensitivity maximization is proposed to extract a minimum size subset of structured residuals that allows for isolation of the faulty sensor. To overcome modelling uncertainty, a robust recursive Bayesian Filter has been employed to process, online, the distance of the residuals from nominal fault directions, providing a fault probability for each sensor. The proposed method has been validated by designing and testing a fault isolation scheme for six aircraft sensors using multi‐flight experimental data of a P92 Tecnam aircraft.