基于IHS变换的多光谱遥感图像有损压缩算法

提出了一种有效的多光谱遥感图像有损压缩算法,该算法根据多光谱遥感图像的数据特点,将IHS变换和整数小波变换相结合进行数据压缩.采用IHS变换去除谱间相关性;采用整数小波变换去除空间相关性,依据各个子带的重要程度,用自适应阈值进行量化,并分别对量化后的数据和重要图表采用固定比特平面编码和游程编码.实验结果表明,算法在保证平均PSNR≥33dB时,获得了较高的压缩比,并且算法硬件实现简单,对内存的需求低.     

一种非一致小波基的超光谱图像压缩方法

传统的三维小波变换在不同维采用相同的小波基且频谱分辨能力也是相同的,这不能与超光谱图像的特性相匹配.本文提出了一种新的采用非一致小波基的去偶结构的三维小波变换的超光谱压缩方法.由于超光谱图像光谱维和空间维具有明显不同的特性而设计的,本文采用一种非一致小波基设计思路,对不同维使用不同的小波基并与去偶的小波变换结构结合在一起进行小波变换.为了获得符合不同维特性的最优的小波基组,本文通过评估指标来测试各种小波基组合的性能.实验结果表明,本文提出的方法能够明显提高超光谱图像的压缩比,且不需牺牲计算复杂度、可扩展性等额外代价.     

波段相关性判定和SPIHT的MODIS图像无损压缩

提出一种结合波段相关性判定和SPIHT编码的方法对MODIS多光谱图像进行无损压缩.首先计算各波段间的相关性,选择相关性强的波段进行预测以去除多光谱图像的谱间冗余;然后对预测后的残差图像进行整数小波变换以去除空间冗余,并用SPRIT算法去除小波系数间的冗余,最后进行算术熵编码.实验结果表明,该方法可对MODIS多光谱图像进行高效无损压缩,优于未进行相关性判定的压缩算法和流行的WinRAR压缩软件.     

基于傅里叶变换的微结构全彩色三维面形重构

分析表明,在傅里叶成像光谱仪的干涉信号中,不仅含有光谱信息,同样也包含三维面形结构信息。因此,本文基于傅里叶变换光谱技术,提出一种彩色三维面形检测方法,不仅可以测量物体表面各个点的光谱反射率分布,还可以获得物体表面三维面形数据,达到三维面形超光谱成像。通过纹理映射将光谱像和三维面形相融合,实现了微结构彩色物体全彩色三维面形数字重构。     

基于梯度权重规则的IHS变换与小波变换结合算法

针对多光谱图像与全色图像的融合,本文在认真分析了IHS变换、小波变换,以及基于梯度绝对值最大准则的IHS变换与小波变换结合算法的基础上,提出了一种基于梯度权重规则的改进算法.在使用小波变换融合多光谱图像I分量与全色图像时,计算二者高频细节分量的梯度作为权重,实现高频细节信息的融合;低频近似分量采用经验调节权系数的方式,运用加权和准则融合获得.融合所得新I'分量与之前多光谱图像IHS变换分离出的色度H和饱和度S进行逆变换,生成最终的融合图像.实验结果表明,该方法在保留多光谱图像光谱信息的基础上,有效地增强了融合图像的空间细节表现能力.     

基于小波变换的图像融合方法研究

图像融合是多传感器信息融合的一个重要分支,小波变换是这一领域研究方法上的重大突破。本文通过对小波变换理论的研究,分析了二维离散小波分解与重构的算法,提出了一种高频小波系数直接替换的算法,并利用MATLAB数学分析工具环境实现全色图像和多光谱图像的融合,实验结果表明这种方法能很好地增强图像的光谱分辨率,便于对图像进行分析和识别。     

一种改进的遥感图像融合方法

针对传统的IHS变换和Mallat算法在融合多光谱图像和高空间分辨力图像时存在的不足,提出了一种将IHS变换和平稳小波变换相结合的遥感图像融合方法;另外,对多光谱图像和高空间分辨力图像因空间分辨力的不同而带来的融合图像中所存在的虚假轮廓问题,提出在融合过程中先定位虚假轮廓出现的位置,然后加以处理的方法.仿真结果表明,本文算法在光谱失真上小于IHS变换法,克服了Mallat算法存在的方块效应,同时较好的抑制了虚假轮廓.     

真正三维小波变换技术及在视频图像编码中的应用

把图像信号当作真正三维信号处理，导出三维空间正交小波基形式。用三维S.Mallat算法来实现三维信号的小波变换，并将其应用在视频图像的编码中。结果表明，低频3－D子带包含有大部分信息，代表图像序列的上下文信息；量化矩阵取值可以调整变化，压缩比最大可达160。因此这种编码方法的性能是十分优越的。     

显微干涉法在MEMS结构三维形貌测试中的应用

鉴于三维形貌在微机电系统(MEMS)测试领域的重要地位及显微干涉法在光学计量领域的高精度性能,开展了显微干涉法在MEMS结构三维形貌测试中的应用研究.基于小波变换极高的去噪性能及处理无载波条纹的优点,包裹相位采用连续小波变换的方法提取.基于展开相位拐点与符号歧义点的单应性,开展了通过检测展开相位拐点并对展开相位进行校正的方法恢复非单调条纹图的真实相位的研究.采用上述方法,成功测试了微梁的静、动态三维形貌及手机芯片的翘曲变形.该项研究为MEMS结构的三维形貌测试提供了高精度且简单快速的测量手段.     

基于整数小波变换与矩阵分解的感兴趣区域可逆水印算法研究

目的针对传统感兴趣区域水印算法抵抗几何攻击能力较弱的缺陷,提出一种基于IWT-Schur的感兴趣区域可逆水印算法。方法首先对载体图像做小波变换,筛选出各子带ROI系数,接着采用Arnold变换加密水印图像,加密水印图像做整数小波变换得到一系列分量。最后结合Schur分解,将水印各分量对应加至载体各子带的ROI。结果采用整数小波变换IWT与矩阵Schur分解的嵌入方式,使得含水印图像的视觉质量良好,算法实现容易。含水印图像没有受到干扰时检测到的水印与原水印一致,含水印图像受到攻击时,也表现出良好的性能,检测到的水印品质较好。结论实验证明,此方案水印提取正确,且感兴趣区域无损恢复。     

Adaptive affinity propagation with spectral angle mapper for semi-supervised hyperspectral band selection

Band selection is a commonly used approach for dimensionality reduction in hyperspectral imagery. Affinity propagation (AP), a new clustering algorithm, is addressed in many fields, and it can be used for hyperspectral band selection. However, this algorithm cannot get a fixed number of exemplars during the message-passing procedure, which limits its uses to a great extent. This paper proposes an adaptive AP (AAP) algorithm for semi-supervised hyperspectral band selection and investigates the effectiveness of distance metrics for improving band selection. Specifically, the exemplar number determination algorithm and bisection method are addressed to improve AP procedure, and the relations between selected exemplar numbers and preferences are established. Experiments are conducted to evaluate the proposed AAP-based band selection algorithm, and the results demonstrate that the proposed method outperforms other popular methods, with lower computational cost and robust results.     

基于光谱特征和纹理特征协同学习的高光谱图像数据分类

高光谱遥感图像中包含有大量的高维数据,传统的有监督学习算法在对这些数据进行分类时要求获取足够多的有标记样本用于分类器的训练.然而,对高光谱图像中大量的复杂地物像元所属类别进行准确标注通常需要耗费极大的人力.在本文中,我们提出了一种基于半监督学习的光谱和纹理特征协同学习(STF-CT)--法,利用协同学习机制将高光谱图像光谱特征和空间纹理特征这两种不同的特征结合起来,用于小训练样本集下的高光谱图像数据分类问题.STF-CT算法充分利用了高光谱图像的光谱和纹理特征这两个独立视图,构建起一种有效的半监督分类方法,用于提升分类器在小训练样本集情况下的分类精度.实验结果表明该算法在小训练样本集下的高光谱地物分类问题上具有很好的效果.     

Quantitative control of neuron adhesion at a neural interface using a conducting polymer composite with low electrical impedance

Tailoring cell response on an electrode surface is essential in the application of neural interfaces. In this paper, a method of controlling neuron adhesion on the surface of an electrode was demonstrated using a conducting polymer composite as an electrode coating. The electrodeposited coating was functionalized further with biomolecules-of-interest (BOI), with their surface concentration controlled via repetition of carbodiimide chemistry. The result was an electrode surface that promoted localized adhesion of primary neurons, the density of which could be controlled quantitatively via changes in the number of layers of BOI added. Important to neural interfaces, it was found that additional layers of BOI caused an insignificant increase in the electrical impedance, especially when compared to the large drop in impedance upon coating of the electrode with the conducting polymer composite.     

基于CEM的高光谱图像小目标检测算法

针对高光谱图像中小目标检测问题,提出了一种基于约束能量最小化(Constrained Energy Minimization,CEM)的目标检测算法.该算法首先对原始图像进行背景信息抑制从而抑制背景地物、突出低概率的小目标,用迭代误差分析的自动端元提取算法找出目标的端元光谱,然后把目标端元光谱代入CEM滤波器得到该目标的检测结果图.用高光谱数据进行了实验研究,并与CEM滤波器进行了比较.结果表明,其检测性能与直接采用CEM方法的检测性能相当,但是相对于CEM方法,该算法不需要目标的先验光谱信息,更具有实用性.     

基于高光谱成像技术的中医舌纹分析算法

针对中医舌诊现代化中使用彩色或灰度图像进行舌纹分析困难的问题,采用高光谱成像系统代替数码相机进行舌图像采集,并提出基于Gabor滤波器的舌纹分析算法.这一算法充分利用高光谱舌图像图、谱两方面的信息进行舌纹分析,并根据特征向量之间归一化的距离对部分典型舌纹进行初步分类.在对474例具有典型舌纹的高光谱舌图像的分类中,除了在来蛇纹与去蛇纹、四直纹与曲虫纹、太阳纹和龟纹、左右撇纹和锯齿纹之间有一定的误分外,整体具有较好的分类精度.这些分类结果表明,基于高光谱图像的舌纹分析方法明显优于基于灰度图像的舌纹分析方法.     

Electronic Structures and Alloying Behaviors of Ferrite Phases in High Co-Ni Secondary Hardened Martensitic Steels

1. IntroductionSuperior combination of strength and fracturetoughness in secondary-hardened high Co-Ni martensitic steels is achieved through the fine precipitation ofmetastable MZC carbides (M=Cr, Mo, Fe) in a ductile Fe-Ni-Co ferrite (tempered martensite) mains.The first group of these steels are the HPg-4-X series,containing gNi, 4Co, ICr, IMc, 0.5V, and 0.2-0.45C(wt pci), followed by HY180 alloys containing 10Ni,SCo, ZCr, IMc and 0.2C (wt pci)l']. In 1978, thegeneral dynamics …     

Detection of gaseous plumes in IR hyperspectral images using hierarchical clustering

The emergence of IR hyperspectral sensors in recent years enables their use in remote environmental monitoring of gaseous plumes. IR hyperspectral imaging combines the unique advantages of traditional remote sensing methods such as multispectral imagery and nonimaging Fourier transform infrared spectroscopy, while eliminating their drawbacks. The most significant improvement introduced by hyperspectral technology is the capability of standoff detection and discrimination of effluent gaseous plumes without need for a clear reference background or any other temporal information. We introduce a novel approach for detection and discrimination of gaseous plumes in IR hyperspectral imagery using a divisive hierarchical clustering algorithm. The utility of the suggested detection algorithm is demonstrated on IR hyperspectral images of the release of two atmospheric tracers. The application of the proposed detection method on the experimental data has yielded a correct identification of all the releases without any false alarms. These encouraging results show that the presented approach can be used as a basis for a complete identification algorithm for gaseous pollutants in IR hyperspectral imagery without the need for a clear background.     

Hyperspectral target detection via discrete wavelet-based spectral fringe-adjusted joint transform correlation

Spectral variability remains a major challenge for target detection in hyperspectral imagery (HSI). Recently, the spectral fringe-adjusted joint transform correlation (SFJTC) technique has been used effectively for hyperspectral target detection applications. In this paper, we propose to use discrete wavelet transform (DWT) coefficients of the signatures as features for detection in order to make the SFJTC technique more insensitive to spectral variability. We devised a supervised training algorithm that uses the pure target signature and randomly selected samples from input scenery to select an optimal set of DWT coefficients for detection. We have inserted target signatures into urban and vegetative hyperspectral scenery with varying levels of spectral variability to explore the performance of our DWT-based SFJTC technique in different operating conditions. Detection results in the form of receiver-operating-characteristic (ROC) curves and area-under-the-ROC (AUROC) curves show that the proposed scheme yields the largest mean AUROC values compared to SFJTC using the original signatures and traditional hyperspectral detection algorithms.     

Hyperspectral diffuse reflectance imaging for rapid, noncontact measurement of the optical properties of turbid materials

We present a method and technique of using hyperspectral diffuse reflectance for rapid determination of the optical properties of turbid media. A hyperspectral imaging system in line scanning mode was used to acquire spatial diffuse reflectance profiles from liquid phantoms made up of absorbing dyes and fat emulsion scatterers over the spectral range of 450-1000 nm instantaneously. The hyperspectral reflectance data were analyzed by using a steady-state diffusion approximation model for semi-infinite homogeneous media. A calibration procedure was developed to compensate the nonuniform instrument response of the imaging system, and a curve-fitting algorithm was used to extract absorption and reduced scattering coefficients (mua and mus', respectively) for the phantoms in the wavelength range from 530 to 900 nm. The hyperspectral imaging system gave good measures of mua and mus' for the phantoms with average fitting errors of 12% and 7%, respectively. The hyperspectral imaging technique is fast, noncontact, and easy to use, which makes it especially suitable for measurement of the optical properties of turbid liquid and solid foods.     

Measurement of the absorption and scattering properties of turbid liquid foods using hyperspectral imaging

A hyperspectral imaging system in line scanning mode was used for measuring the absorption and scattering properties of turbid food materials over the visible and near-infrared region of 530-900 nm. An instrumental calibration procedure was developed to compensate for the nonuniform instrument response of the imaging system. A nonlinear curve-fitting algorithm for a steady-state diffusion theory model was proposed to determine absorption (mua) and reduced scattering coefficients (mu's) from the spatially resolved hyperspectral reflectance profiles. The hyperspectral imaging system provided good measurement of mua and mu's for the simulation samples made of Intralipid scattering material and three absorbers (blue dye, green dye, and black ink) with average fitting errors of 16% and 11%, respectively. The optical properties of the fruit and vegetable juices and milks were determined. Values of the absorption and reduced scattering coefficient at 600 nm were highly correlated to the fat content of the milk samples with the correlation coefficient of 0.995 and 0.998, respectively. Compared to time-resolved and frequency-domain techniques, the hyperspectral imaging technique provides a faster and simpler means for measuring the optical properties of turbid food and agricultural products.