MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspectral image. According to the characteristics and 3-Dimensional (3-D) feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures: Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient inte- gration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling (RIBSR) method, is proposed to accomplish data resampling in spectral domain by util- izing the property of ratio image. And a new fusion rule, Average and Substitution (A&S) rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral character- istics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A&S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.

Application of Wavelet Algorithm to Spectral Analysis of Oceanic Waves and Offshore Structure Responses

Fourier transform （FF） is a commonly used method in spectral analysis of ocean wave and offshore structure responses, but it is not suitable for records of short length. In this paper another method, wavelet transform （WT）, is applied to ＇analyze the data of short length. The Morlet wavelet is employed to calculate the spectra density functions for wave records and simulated Floating Production Storage and Offloading （FPSO） vessels＇ responses. Computed wave data include simulated wave data based on JONSWAP spectrum and the recorded data of Storm 149 from North Alwyn. Wavelet method is validated by comparing the statistical characteristics by WF method and those by fast Fourier transform （FFT） method with those of target spectra. The spectral density fnnctions＇ shapes calculated by WT are less malformed and have less error of statistical characteristics compared with those by FT especially when the record lengths decrease.

WAVELET BASED SPECTRAL CORRELATION METHOD FOR DPSK CHIP RATE ESTIMATION

A wavelet-based spectral correlation algorithm to detect and estimate BPSK signal chip rate is proposed. Simulation results show that the proposed method can correctly estimate the BPSK signal chip rate, which may be corrupted by the quadratic characteristics of the spectral correlation function, in a low SNR environment.

Denoising of hyperspectral imagery by cubic smoothing spline in the wavelet domain

The acquired hyperspectral images(HSIs) are inherently affected by noise with band-varying level,which cannot be removed easily by current approaches.In this study,a new denoising method is proposed for removing such kind of noise by smoothing spectral signals in the transformed multiscale domain.Specifically,the proposed method includes three procedures:1) applying a discrete wavelet transform(DWT) to each band;2) performing cubic spline smoothing on each noisy coefficient vector along the spectral axis;3) reconstructing each band by an inverse DWT.In order to adapt to the band-varying noise statistics of HSIs,the noise covariance is estimated to control the smoothing degree at different spectral positions.Generalized cross validation(GCV) is employed to choose the smoothing parameter during the optimization.The experimental results on simulated and real HSIs demonstrate that the proposed method can be well adapted to band-varying noise statistics of noisy HSIs and also can well preserve the spectral and spatial features.

Determination of micro yttrium in an ytterbium matrix by inductively coupled plasma atomic emission spectrometry and wavelet transform

In the determination of trace yttrium (Y) in an ytterbium (Yb) matrix byinductively coupled plasma atomic emission spectrometry (ICP-AES), the most prominent line ofyttrium, Y 371.030 nm line, suffers from strong interference due to an emission line of ytterbium.In mis work, a method based on wavelet transform was proposed for the spectral interferencecorrection. Haar wavelet was selected as the mother wavelet. The discrete detail after the thirddecomposition, D3, was chosen for quantitative analysis based on the consideration of bothseparation degree and peak height. The linear correlation coefficient between the height of the leftpositive peak in D3 and the concentration of Y was calculated to be 0.9926. Six synthetic sampleswere analyzed, and the recovery for yttrium varied from 96.3 percent to 110.0 percent. The amountsof yttrium in three ytterbium metal samples were determined by the proposed approach with an averagerelative standard deviation (RSD) of 2.5 percent, and the detection limit for yttrium was 0.016percent. This novel correction technique is fast and convenient, since neither complicated modelassumption nor time-consuming iteration is required. Furthermore, it is not affected by thewavelength drift inherent in monochromators that will severely reduce the accuracy of resultsobtained by some chemometric methods.

Statistical analysis of Epileptic activities based on Histogram and Wavelet-Spectral entropy

Epilepsy is a chronic neurological disorder which is identified by successive unexpected seizures. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its normal or epileptic activity. In this work EEG and its frequency sub-bands have been analysed to detect epileptic seizures. A discrete wavelet transform (DWT) has been applied to decompose the EEG into its sub-bands. Applying histogram and Spectral entropy approaches to the EEG sub-bands, normal and abnormal states of brain can be distinguished with more than 99% probability.

Hilbert-Huang transform and wavelet analysis of time history signal

The brief theories of wavelet analysis and Hilbert-Huang transform (HHT) are introduced firstly in the present paper. Then several signal data were analyzed by using wavelet and HHT methods, respectively. The comparison shows that HHT is not only an effective method for analyzing non-stationary data, but also is a useful tool for examining detailed characters of time history signal.

高分辨率层序地层学在北部湾盆地重点层系选段中的应用

北部湾盆地是南海北部五大含油气盆地之一,油气资源潜力大、有较好的勘探前景,但是北部湾盆地三级构造单元海中凹陷古近系涠洲组地质研究程度较低、地层厚度大,严重制约了海中凹陷的油气勘探。高分辨率层序地层学在低勘探程度区域是一种有效的地层划分手段。依托三维地震资料、测井资料、岩心资料,在地震剖面二、三级层序界面和测井层序界面识别的基础上,采用最大熵谱分析、小波变换分析、岩心分析方法,对海中凹陷涠洲组进行了高分辨率层序识别与划分,研究表明最大熵谱分析对于三、四级层序界面具有较高的分辨性,小波变换可以进行四—六级及更高级别旋回分析,而岩心资料可以进行短期、超短期旋回分析。综合三维地震、测井、最大熵谱分析、小波变换技术和岩心资料将涠洲组划分为1个二级层序,包括6个三级层序、13个四级层序,并且根据13个四级层序含砂率对比,涠三段SQ6-LST四级层序含砂率最高,涠三段SQ6-HST四级层序次之,勘探潜力较大。本研究对低勘探程度凹陷重点层系选段具有一定指导意义。

突发脉冲干扰下的白鲸哨声端点检测方法

针对圈养条件下大量突发脉冲干扰影响白鲸哨声检测的问题,提出了一种可调Q因子小波变换(TQWT)结合自适应谱聚类的无监督哨声信号检测方法。对原始信号做高Q因子的小波分解,并以平均小波系数作为检验统计量完成哨声端点粗检测,排除静默时间段与低强度脉冲干扰;在此基础上使用局部密度自适应谱聚类进一步区分哨声与强脉冲干扰,完成白鲸哨声检测。使用实采的白鲸信号测试该检测器性能并计算其F_(1)分数。该检测器在两段测试信号下分别获得了0.9487和0.9429的F_(1)分数,且在大数据量情况下明显优于由k-means聚类或传统谱聚类构成的检测器,具有更高的鲁棒性。

基于自适应Autogram+OSF滤波的电机轴承故障诊断

自相关谱峭度图Autogram方法可以对初始信号非周期脉冲与噪声进行限制的方式测定周期性脉冲,获得更精确的最优频带。设计了一种自适应Autogram方法,通过顺序统计滤波(OSF)方法完成电机轴承故障信号傅里叶谱的包络处理。研究结果表明:采用自适应Autogram方法进行处理时具备良好可行性。采用本方法测试佳解调频带的效果比滤波器快速谱峭度方法的性能更优。相对自适应Autogram方法并未形成明显的二倍与三倍频特征频率,产生了众多干扰分量。

基于离散小波变换特征的农产品叶片表面农药残留检测方法

农药残留检测方法易受到叶片图像空间噪声的干扰,检测精度较低。提出基于离散小波变换特征的农产品叶片表面农药残留检测方法。采用多模态融合网络对农产品叶片表面特征向量进行提取,在网络模型中引入注意力模块对特征向量进行非对称模态融合处理,以消除空间噪声,并通过对叶片图像的边缘进行过滤与校正,以分割叶片图像边缘,结合离散小波变换特征算法对原始图像进行逐级分解变换,并求取叶片表面光谱反射率,以此确定叶片表面感兴趣区域,基于此,利用特征光谱处理方法对农药的光谱进行判别,以此实现农药残留检测。对比试验结果表明,所提方法对于农产品叶片表面农药残留检测具有较高的检测精度。

Analysis of Spectral Characteristics Based on Optical Remote Sensing and SAR Image Fusion

Because of cloudy and rainy weather in south China, optical remote sensing images often can't be obtained easily. With the regional trial results in Baoying,Jiangsu province, this paper explored the fusion model and effect of ENVISAT/SAR and HJ-1A satellite multispectral remote sensing images. Based on the ARSIS strategy, using the wavelet transform and the Interaction between the Band Structure Model(IBSM), the research progressed the ENVISAT satellite SAR and the HJ-1A satellite CCD images wavelet decomposition, and low/high frequency coefficient reconstruction, and obtained the fusion images through the inverse wavelet transform.In the light of low and high-frequency images have different characteristics in different areas, different fusion rules which can enhance the integration process of selfadaptive were taken, with comparisons with the PCA transformation, IHS transformation and other traditional methods by subjective and the corresponding quantitative evaluation. Furthermore, the research extracted the bands and NDVI values around the fusion with GPS samples, analyzed and explained the fusion effect. The results showed that the spectral distortion of wavelet fusion, IHS transform, PCA transform images was 0.101 6, 0.326 1 and 1.277 2, respectively and entropy was14.701 5, 11.899 3 and 13.229 3, respectively, the wavelet fusion is the highest.The method of wavelet maintained good spectral capability, and visual effects while improved the spatial resolution, the information interpretation effect was much better than other two methods.

基于IEWT-MOMEDA-FSC的滚动轴承故障诊断

针对滚动轴承故障信号常伴有噪声干扰且故障特征难以提取的问题,本文提出一种基于改进经验小波变换(IEWT)、多点优化最小熵解卷积(MOMEDA)和快速谱相关(FSC)的滚动轴承故障诊断方法.首先,将原始信号进行快速谱相关分析得到增强包络谱,通过增强包络谱的极值点来自适应地划分频谱,以分割的频谱为边界构建小波滤波器组将信号分解为多个IMF分量,利用相关峭度准则筛选出有效的分量进行叠加;其次,用MOMEDA对其进行降噪处理,将降噪后的信号进行快速谱相关分析,得到增强包络谱图;最后,将增强包络谱图中幅值较高的频率与故障频率对比,判定其失效形式,用所提出的方法对实测轴承故障信号进行分析验证.结果表明,所提出的方法能有效降低噪音干扰且增强信号故障冲击特性,在噪声环境下具有较强的故障特征提取能力.

川东地区寒武系高台组白云岩-蒸发岩共生地层高频层序划分及地质意义

白云岩-蒸发岩共生地层中油气资源丰富。基于层序地层学原理,结合钻井、薄片等资料,在三级层序划分的基础上,采用INPEFA和小波变换技术对川东地区寒武系高台组白云岩-蒸发岩共生地层的高频层序进行识别与划分,并探讨了其地质意义。研究结果表明:(1)川东地区寒武系高台组白云岩-蒸发岩共生地层可划分为3个三级层序,自下而上分别为Sq1,Sq2,Sq3,可进一步划分为6个四级层序(ssq1—ssq6)。高台组主要为碳酸盐岩台地沉积环境,自西向东依次发育混积台地、局限-蒸发台地、开阔台地相沉积。(2)研究区高台组的岩性组合序列包括白云岩上覆厚层蒸发岩(A)、厚层白云岩夹蒸发岩(B)、白云岩与蒸发岩互层(C)和蒸发岩上覆厚层白云岩(D)等4种类型。白云岩-蒸发岩共生地层发育在四级层序的接触界面,且多发育于层序内部的高位体系域,反映了海平面较强烈的扰动,尤其是海平面的相对下降有利于白云岩和蒸发岩沉积。(3)研究区高台组白云岩-蒸发岩共生地层的岩性组合序列在GR_(inpefa)曲线和小波变换曲线上均具有响应特征,对应的64尺度小波系数曲线波动平缓,能量团为弱黄蓝色,GR_(inpefa)曲线以“缺口”的形式表现出蒸发岩与白云岩的岩性差异。

利用叶片高光谱反射率预测棉花叶绿素含量

为提高棉花叶绿素含量预测的准确性,利用连续小波分析和传统光谱变换对棉花叶片原始光谱进行分解和变换,以特征小波系数和光谱特征波段为自变量,并利用单变量、逐步回归和偏最小二乘法建立反演棉花叶片叶绿素含量的数学模型。结果显示,不同的光谱处理方法使得棉花叶片叶绿素和光谱反射率的相关性都有不同程度的提升,对于传统光谱变换,倒数对数一阶微分lg(1/R′)对棉花叶片叶绿素相关性提高了0.41。结果表明,连续小波分析在信息降噪和挖掘特征信息方面优于传统光谱模型,建立的模型RPD>2,具有很好的稳定性,对样本数据都具很好的预测能力。

Novel Face Recognition Method by Combining Spatial Domain and Selected Complex Wavelet Features

A novel face recognition method based on fusion of spatial and frequency features was presented to improve recognition accuracy. Dual-Tree Complex Wavelet Transform derives desirable facial features to cope with the variation due to the illumination and facial expression changes. By adopting spectral regression and complex fusion technologies respectively, two improved neighborhood preserving discriminant analysis feature extraction methods were proposed to capture the face manifold structures and locality discriminatory information. Extensive experiments have been made to compare the recognition performance of the proposed method with some popular dimensionality reduction methods on ORL and Yale face databases. The results verify the effectiveness of the proposed method.

THE WAVELET ANALYSIS METHOD OF STATIONARY RANDOM PROCESSES

The spectral analysis of stationary random processes is studied by using wavelet transform method. On the basis of wavelet transform, the conception of time-frequency power spectral density of random processes and time-frequency cross-spectral density of jointly stationary random processes are presented. The characters of the time-frequency power spectral density and its relationship with traditional power spectral density are also studied in details.

基于判别分析的中药材快速鉴别研究

该文基于红外光谱数据研究中药材产地鉴别问题,首先利用离散小波变换处理原始信号,扩大不同产地光谱图差异,精准筛选出特征波段,然后使用神经网络和判别分析鉴别药材产地。由仿真结果可知,神经网络识别准确率为62%,效果不太理想,而判别分析识别准确率高达89.9%,相较前种方法有较大提升,所以使用判别分析可以快速实现中药材产地鉴别。

优化光谱输入量的土壤有机质随机森林预测模型

以往的土壤有机质预测研究往往只提取一种光谱输入量,忽略了不同光谱输入量之间的互补性。为探究光谱输入量在预测土壤有机质时的最佳组合,以及不同光谱输入量在离散小波变换不同分解尺度下的变化趋势,该研究以宝清县土壤有机质为研究对象,对光谱反射率进行离散小波变换,对各个分解尺度下的特征光谱提取光谱特征参数、光谱指数以及主成分并分别组合,基于8种光谱输入量建立随机森林模型进行土壤有机质预测。结果表明:1)利用不同光谱输入量预测有机质的精度均高于直接使用光谱反射率建模的精度,将不同光谱输入量组合可以提升预测效果,单个光谱输入量中主成分的预测效果最好,组合中光谱特征参数和主成分的组合预测效果最好;2)随着分解尺度的变化,不同光谱输入量的预测精度的变化趋势也不同,并且单个光谱输入量的变化趋势也会影响该光谱输入量组合的变化趋势;3)所有预测结果中,精度最高的是分解尺度为6时光谱特征参数与主成分的组合,R^(2)达到0.78,均方根误差达到1.32%,可以较好地预测土壤有机质。研究结果说明光谱输入量结合离散小波变换预测土壤有机质是可行的,可以为土壤有机质的预测提供可靠思路。

基于改进经验小波变换的局部放电荧光信号去噪

由于现场环境的复杂性,局部放电(partial discharge,PD)检测伴随着大量噪声干扰,易出现PD漏报与误报现象,影响电力设备后续运维工作。文中采用荧光光纤PD检测法,提出了基于谱峭度和改进经验小波变换(empirical wavelet transform,EWT)的自适应PD荧光信号去噪算法。首先利用快速谱峭度图确定荧光信号傅里叶频谱的紧支撑区域边界,随后对含噪荧光信号进行EWT分解并获得荧光信号所在的有用信号分量,最后对有用信号分量采用小波阈值法去除残留噪声,得到去噪后的PD荧光信号。利用该方法对仿真荧光信号进行去噪分析,并将去噪结果与经验模态分解-小波变换(empirical mode decomposition-wavelet transform,EMD-WT)法和EWT法进行对比,结果表明,该方法在信噪比、均方根误差和归一化相关系数等指标方面都有所提升,证明了该方法具有良好的去噪效果。此外,实测信号的去噪结果表明,该方法的降噪率优于EMD-WT法和EWT法,具有较好的噪声抑制能力。