实现谱峰自动识别的句法方法

本文描述一种用于自动识别数字谱数据序列中的谱峰的句法方法.为便于谱峰识别,先 将谱数据序列编码为谱峰描述语句,然后用一种谱峰识别自动机将其中的谱峰一一选出.文 中详细说明了产生谱峰描述语言的谱峰文法和识别谱峰的谱峰识别自动机,同时给出了实验 结果.     

地物波谱数据库应用方法及遥感应用现状

地物波谱数据库在遥感信息提取中具有重要的应用价值,本文归纳和总结了常用的国内外通用型地物波谱数据库与专业型地物波谱数据库的发展现状。在对有关波谱库遥感应用文献进行计量分析的基础上,综述了地物波谱数据库遥感应用的四种主要方法波谱特征分析、光谱匹配识别、混合像元分解以及参数提取建模,阐述了地物波谱数据库在地物分类、目标识别及参数反演中的应用。从当前所处的遥感"大数据"时代背景出发,亦对地物波谱数据库的建设趋势与应用潜力进行了展望。     

Review of Methods and Remote Sensing Cases Using Spectral Library

Ground object spectral libraries play a significant role in remote sensing information extraction. This paper investigates the domestic and foreign spectral libraries frequently-used, including the general spectral libraries and the professional spectral libraries. Based on the biliometric analysis of the literatures about remote sensing applications based on spectral libraries, four kinds of methods are summarized, including spectral feature analysis, spectral matching, spectral mixture analysis and quantitative remote sensing modeling. Some remote sensing applications based on spectral libraries, such as ground object classification, target identification and land surface parameters inversion, are also summarized. From the background of remote sensing big data, the developing trends and application potential of the ground object spectral library are prospected at the end.     

Minimax Filtering of Sequences with Periodically Stationary Increments

The authors consider the problem of optimal filtering of functionals that depend on unknown values of the stochastic sequence with periodically stationary increments based on observations of the sequence with a stationary noise. For sequences with known spectral densities, formulas are obtained for the root-mean-square errors and spectral characteristics of the optimal estimates of the functionals. Formulas that determine the least favorable spectral densities and minimax (robust) spectral characteristics of the optimal linear estimates of functionals are proposed in the case where spectral densities of the sequence are not known exactly while some sets of feasible spectral densities are given.

     

基于核方法的光谱角制图模型及其在高光谱图像分割中的应用

在分析传统光谱角制图方法的几何属性和相似性度量特点的基础上，提出一种新的光谱相似性度量方法。该方法首先利用非线性变换将原始光谱向量变换到一个新的特征空间。其次，在特征空间中，利用协方差矩阵的非零特征向量构造了一组正交基，并将变换后的光谱向量投影到特征空间中的正交基上。高光谱图像分割实验结果表明该方法在光谱相似性度量上优于传统的光谱角制图方法。     

Content‐Independent Multi‐Spectral Display Using Superimposed Projections

Many works focus on multi‐spectral capture and analysis, but multi‐spectral display still remains a challenge. Most prior works on multi‐primary displays use ad‐hoc narrow band primaries that assure a larger color gamut, but cannot assure a good spectral reproduction. Content‐dependent spectral analysis is the only way to produce good spectral reproduction, but cannot be applied to general data sets. Wide primaries are better suited for assuring good spectral reproduction due to greater coverage of the spectral range, but have not been explored much. In this paper we explore the use of wide band primaries for accurate spectral reproduction for the first time and present the first content‐independent multi‐spectral display achieved using superimposed projections with modified wide band primaries. We present a content‐independent primary selection method that selects a small set of n primaries from a large set of m candidate primaries where m > n. Our primary selection method chooses primaries with complete coverage of the range of visible wavelength (for good spectral reproduction accuracy), low interdependency (to limit the primaries to a small number) and higher light throughput (for higher light efficiency). Once the primaries are selected, the input values of the different primary channels to generate a desired spectrum are computed using an optimization method that minimizes spectral mismatch while maximizing visual quality. We implement a real prototype of multi‐spectral display consisting of 9‐primaries using three modified conventional 3‐primary projectors, and compare it with a conventional display to demonstrate its superior performance. Experiments show our display is capable of providing large gamut assuring a good visual appearance while displaying any multi‐spectral images at a high spectral accuracy.     

Low-rate multimode multiband spectral coding of speech

At bit rates of 4 kbps and below, conventional time-domain algorithms such as CELP fail to retain high voice quality and robust performance against background noise as their waveform-matching ability is curtailed by the severely limited codebook space. Spectral coding, on the other hand, offers an effective parametric model, amenable for low-rate implementation. Instead of performing waveform matching, spectral coders preserve only the perceptually important spectral attributes of the speech signal. Spectral coding algorithms encompass a broad family of emerging low-rate speech coding techniques, the common goal being the representation of the short-term spectrum of input speech with a limited set of spectral parameters and the synthesis of the output speech with a set of sinusoids. Pitch, frequency-domain voicing information, and a varying number of spectral magnitudes are the usual parameters of spectral coders. In this paper, we present the enhanced multiband excitation (EMBE) coder as an illustration of this new generation of low-rate spectral coders. The distinguishing features of EMBE are: (a) signaladaptive multimode spectral modeling and parameter quantization, (b) two-band signal-adaptive frequency-domain voicing decision, (c) a novel VQ scheme for the efficient encoding of the variable-dimension spectral magnitude vectors at low-rates, and (d) multi-class selective protection of spectral parameters from channel errors. A 4 kbps implementation of the EMBE spectral coding algorithm with 2.9 kbps source coding and 1.1 kbps for channel coding was specifically designed for satellite-based communication systems, targeting good voice quality at low bit rates and robust performance against channel errors. Fundamental concepts of the EMBE spectral coding algorithm, implementation details, and performance comparisons of the 4 kbps EMBE coder with earlier coders are reported.     

重复轨道InSAR处理中的频域滤波研究

在重复轨道InSAR(synthetic aperture radar interferom etry)处理干涉测量中,干涉图像对在方位向和距离向的频谱偏移是重要的去相干因素,严重影响干涉图的质量,必须对主辅图像对采取适当的频域滤波。本文提出了一种方位向频域滤波和距离向频域滤波的处理流程,即根据频域特征选择在精配准前后做频域滤波。它既能保证精配准的准确度,又能充分降低频谱偏移导致的去相干,从而保证干涉图的高质量。利用4组不同时间间隔和垂直基线距的ERS-1/2 SLC(single look comp lex)数据对进行实验验证,通过对频域滤波前后相干图和残差点的对比分析,验证了本处理流程的有效性。     

非刚性三维形状匹配中基于谱分析的形状描述符综述

基于谱分析的形状描述符在非刚性三维形状匹配中取得了较好的匹配效果，引起了研究者的广泛关注.谱分析是基于流形上拉普拉斯贝尔特拉米算子谱分解的一种内蕴形状分析方法.谱形状描述符和谱距离分布函数是最主要的两类谱分析形状描述符，它们具有不同的数学性质和物理意义.基于两类不同的形状描述符，给出了详细的方法分析及其在形状匹配中的应用.首先，给出了应用基于谱分析的形状描述符的非刚性三维形状匹配框架，介绍了几种常用的谱形状描述符及谱距离分布函数的基本思想和计算方法；然后，分析比较了这些形状描述符的优缺点及应用场景，为研究者选择基于谱分析的形状描述符提供参考；最后，通过实验对比了不同基于谱分析的形状描述符的算法鲁棒性、时间耗费及非刚性匹配性能，以此推动谱分析形状描述符的应用进程.     

m序列频谱免疫度的研究

频谱免疫度是度量周期序列抵抗离散傅里叶频谱攻击的重要指标。周期序列的频谱免疫度越大,抵抗离散傅里叶频谱攻击的能力越强。通过搜索[m]序列的零化子,计算其频谱重量,提出猜想：[n]级[m]序列的零化子最低频谱重量是[n+1],而该[m]序列的补序列恰恰是其一个最低频重零化子。研究了[m]序列零化子及其补序列的性质,从理论上证明了该猜想。分析了[m]序列补序列的零化子性质,得出结论：[n]级[m]序列的频谱免疫度为[n];说明了[m]序列难以抵抗离散傅里叶频谱攻击。     

基于经验模分解高光谱图像数据尺度变换的光谱保真性分析

经验模分解是具有自适应性特点的尺度变换方法。高光谱图像数据近乎连续的光谱是开展信息提取的重要信息来源,然而尺度变换会使光谱发生变化。因此,分析EMD尺度变换后高光谱图像数据的光谱保真性具有重要意义。应用CHRIS高光谱图像数据,使用光谱相关系数、光谱偏差、光谱相对偏差和光谱角等评价指标,开展EMD升尺度图像及其典型地物的光谱保真性实验,并将EMD与Mallat小波变换光谱保真性比较。实验结果得出:11~10级EMD尺度变换后图像整体光谱保真性都较好,相关系数均在0.979以上,偏差小于55,相对偏差小于0.036,光谱角在0.041以内;2图像光谱保真性随EMD尺度变换次数增加而略有降低,且前4级变换光谱失真相对明显,后续降幅微弱;31~10级尺度变换后7种湿地典型地物的光谱保真性都较好,其中芦苇和河流的光谱保真程度较突出,养殖水面的相对不理想;4EMD与小波变换光谱保真性比较,随着变换次数的增加EMD表现出相对稳定且较小的光谱失真。     

Continuity of the spectral factorization on a vertical strip

The continuity of the mapping which associates a spectral factor to a spectral density is investigated. This mapping can be defined on several classes of spectral densities and spectral factors. For the usual largest class of spectral densities, i.e., essential bounded functions on the imaginary axis that are bounded away from zero, it is known that this mapping is not continuous. It is shown here that for slightly smaller, but still generic class the mapping becomes continuous.     

Integration of spatial-spectral information for the improved extraction of endmembers

Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial-spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast endmembers, thus improving the potential for these endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar endmembers, allowing for separation of endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of endmembers. The results show that the SSEE method is an effective approach to extracting image endmembers. Specific improvements include the extraction of physically meaningful, low contrast endmembers that occupy unique image regions.     

Spatio-spectral fusion of satellite images based on dictionary-pair learning

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.     

Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces

Remote sensing technique has become the most efficient and common approach to estimate surface vegetation cover. Among various remote sensing algorithms, spectral mixture analysis (SMA) is the most common approach to obtain sub‐pixel surface coverage. In the SMA, spectral endmembers (the number of endmembers may vary), with invariant spectral reflectance across the whole image, are needed to conduct the mixture procedure. Although the nonlinear effect in quantifying vegetation spectral reflectance was noticed and sometimes addressed in the SMA analysis, the nonlinear effect in soil spectral reflectance is seldom discussed in the literature. In this paper, we investigate the effects of vegetation canopy on the inter‐canopy soil spectral reflectance via mathematical modelling and field measurements. We identify two mechanisms that lead to the difference between remotely sensed apparent soil spectral reflectance and actual soil spectral reflectance. One is a canopy blockage effect, leading to a reduced apparent soil spectral reflectance. The other is a canopy scattering effect, leading to an increased apparent soil spectral reflectance. Without correction, the first (second) mechanism causes an overestimated (underestimated) areal coverage of the low‐spectral‐reflectance endmember. The overall effect of canopy to soil, however, tends to overestimate fractional vegetation cover due to the relative significance of the canopy blockage effect, even though the two mechanisms vary with spectral wavelengths and spectral difference between different vegetation and soil. For the SMA of vegetated surface using multiple‐spectral remote sensing imagery (e.g., LandSat), it is recommended that infrared bands of low vegetation spectral reflectance (e.g. band 7) be first considered; if both visible and infrared bands are used, combination of bands 3, 4, and 5 is appropriate, while use of all six bands could overestimate fraction vegetation cover.     

切比契夫谱元素局部混合基函数构造

针对切比契夫谱方法,该文首次构造了两类局部混合基函数,据此发展了一种新的谱元素方法:在元素端点采用局部拉格朗日插值基,元素内部采用经调整后的切比契夫多项式。这里的两类混合基函数在计算精度上可与传统的拉格朗日基相媲美,而且元素矩阵具有稀疏特征和数据重用性。该文给出的局部混合基函数对传统的谱元素方法进行了扩充。     

Spectral methods on triangles and other domains

This article shows how to obtain multidimensional spectral methods as a warped product of one-dimensional spectral methods, thus generalizing direct (tensor) products. This generalization includes the fast Fourier transform. Applications are given for spectral approximation on a disk and on a triangle. The use of the disk spectral method for simulating the Navier-Stokes equations in a periodic pipe is detailed. The use of the triangle method in a spectral element scheme is discussed. The degree of approximation of the triangle method is computed in a new way, which favorably compares with the classical approximation estimates.     

谱聚类算法研究综述

聚类分析是一种常见的分析方法,谱聚类作为聚类分析的一支,因其不受样本形状约束等特点备受瞩目。为及时掌握当前谱聚类算法研究动态,通过对比分析众多谱聚类优化算法,从半监督学习、二阶段聚类算法选择、算法执行效率优化等三个角度,将谱聚类优化算法分为三类,并对每类算法的优化思想进行综述。介绍经典多路谱聚类与基本理论,并分析相似矩阵及其特征值、特征向量选取原因及影响,旨在明确特征矩阵的重要性与优化的必要性。基于算法改进策略差异,梳理并总结每类算法的改进思想、研究现状及优缺点。在UCI数据集与手写体数据集上,针对谱聚类算法与优化算法进行实验对比,并对谱聚类优化算法的未来研究方向进行展望。     

HJ-1 A高光谱数据的条带噪声去除方法研究

针对环境减灾小卫星的高光谱图像条带的特点,提出了基于光谱空间连续性的倾斜条带去除方法。高光谱数据的光谱分辨率达到纳米级,光谱波段多,在一定范围内可以连续成像,具有光谱空间的连续性,基于光谱空间连续性的条带去除方法利用了光谱空间连续性的这一重要特点。本文在考虑了图像条带噪声的倾斜角度的基础上,成功地将该方法应用于批量的环境减灾小卫星2级高光谱数据,进行了相对辐射校正的研究,并将相邻列均衡方法应用于单幅环境减灾小卫星2级高光谱数据,对比二者的单幅图像条带去除效果,结果证明基于光谱空间连续性的条带去除方法较相邻列均衡方法更适合于对环境减灾小卫星的2级高光谱数据进行条带噪声的去除。     

On testing for serial correlation of unknown form using wavelet thresholding

Omnibus procedures for testing serial correlation are developed, using spectral density estimation and wavelet shrinkage. The asymptotic distributions of the wavelet coefficients under the null hypothesis of no serial correlation are derived. Under some general conditions on the wavelet basis, the wavelet coefficients asymptotically follow a normal distribution. Furthermore, they are asymptotically uncorrelated. Adopting a spectral approach and using results on wavelet shrinkage, new one-sided test statistics are proposed. As a spatially adaptive estimation method, wavelets can effectively detect fine features in the spectral density, such as sharp peaks and high frequency alternations. Using an appropriate thresholding parameter, shrinkage rules are applied to the empirical wavelet coefficients, resulting in a non-linear wavelet-based spectral density estimator. Consequently, the advocated approach avoids the need to select the finest scale J, since the noise in the wavelet coefficients is naturally suppressed. Simple data-dependent threshold parameters are also considered. In general, the convergence of the spectral test statistics toward their respective asymptotic distributions appears to be relatively slow. In view of that, Monte Carlo methods are investigated. In a small simulation study, several spectral test statistics are compared, with respect to level and power, including versions of these test statistics using Monte Carlo simulations.