The comparative analytical performance of the Beagle 2 X‐ray Spectrometer for in situ geochemical analysis on Mars

The Beagle 2 X‐ray Spectrometer (B2 XRS) instrument was part of the Beagle 2 Mars lander payload and intended to perform in situ geochemical analyses of geological materials on Mars. The analytical performance of a spare version of the B2 XRS was compared with (1) a portable X‐ray fluorescence (PXRF) spectrometer designed to perform terrestrial fieldwork and (2) a laboratory‐based wavelength‐dispersive (WD‐XRF) system used to produce high quality geochemical data. The criteria used to assess the performance were based on fitting precision, accuracy and detection limit, determined from the analysis of international geochemical reference materials. The fitting precision of the B2 XRS and PXRF was found to be in agreement with the Horwitz function (a benchmark relating the analysed concentration of an analyte to its uncertainty) over 4 orders of magnitude of concentration range from 10^?1 to 10^?5 g/g. The PXRF generally had a better fitting precision than the B2 XRS because of its better resolution. In order of improving accuracy, the spectrometers generally are ranked B2 XRS, PXRF and WD‐XRF for various major and trace elements. A limiting factor in the accuracy of the B2 XRS was the application of the algorithm used for its quantitative analysis. The detection limits for the spectrometers ranked in the same order as the accuracy as a result of improving signal‐to‐noise ratio (SNR) of elemental peaks, which is a direct consequence of improving resolution between these spectrometers. Overall, the B2 XRS was found to have a favourable analytical performance compared to the benchmark spectrometers, despite having met considerable design constraints and qualification tests as a planetary instrument. Copyright © 2009 John Wiley & Sons, Ltd.     

GLCM和DWT特征在打印文件机源认证中的应用

为了有效解决打印文件机源认证问题,提出了一种基于统计纹理特征选择的打印文件机源认证方法。综合考虑打印字符图像的空间域和时频域特性,将GLCM和DWT统计纹理特征进行组合,运用ReliefF算法实现组合特征的初选,二次特征选择使用SVM-RFE算法。文中实验结果表明,在英文相同字有重复样本集和中文不同字无重复样本集上的分类准确率分别为95.20%和75.00%;特征组合与特征选择有利于提高打印文件机源认证的分类鉴别性能。     

基于改进DeepLabv3+的无人车夜间红外图像语义分割

为了增强无人车对夜间场景的理解能力,针对无人车在夜间获取的红外图像,提出了一种基于改进DeepLabv3+网络的无人车夜间红外图像语义分割算法。由于自动驾驶场景中的对象往往显示出非常大的尺度变化,该算法在DeepLabv3+网络的基础上,通过引入密集连接的空洞卷积空间金字塔模块,使网络生成的多尺度特征能覆盖更大的尺度范围。此外,该算法将编码器模块的多层结果拼接在译码器模块中,以恢复更多在降采样过程中丢失的空间信息和低级特征。通过端到端的学习和训练,可直接用于对夜间红外图像的语义分割。实验结果表明,该算法在红外数据集上的分割精度优于原DeepLabv3+算法,平均交并比达到80.42,具有良好的实时性和准确性。     

Prospective results of CHANG'E-2 X-ray spectrometer

The Chinese second lunar satellite CE-2, which carries an X-ray spectrometer (XRS), will be launched at the end of 2010.In order to estimate the scientific results of XRS, we simulate the anticipated lunar X-ray spectra observed by XRS by using the expected mean solar X-ray flux in 2011.We also obtain the integration time and the spatial resolution required to achieve a certain significance level for the major lunar rock-forming elements in different solar activity conditions.It is expected that a spatial resolution of finer than 100 kilometers can be achieved for elements Mg, Al, Si, Ca, Ti, and Fe.     

Prospective results of CHANG'E-2 X-ray spectrometer

The Chinese second lunar satellite CE-2, which carries an X-ray spectrometer （XRS）, will be launched at the end of 2010. In order to estimate the scientific results of XRS, we simulate the anticipated lunar X-ray spectra observed by XRS by using the expected mean solar X-ray flux in 2011. We also obtain the integration time and the spatial resolution required to achieve a certain significance level for the major lunar rock-forming elements in different solar activity conditions. It is expected that a spatial resolution of finer than 100 kilometers can be achieved for elements Mg, Al, Si, Ca, Ti, and Fe.     

一种空域和频域相结合的运动图像亚像素配准技术

针对视频图像运动检测问题,提出了一种结合空域灰度投影和频域相位相关的亚像素图像配准方法。首先,采用灰度投影算法在空域对运动图像进行粗配准,即在图像行和列方向上计算图像灰度投影特征数据,根据灰度相关函数最小化准则,估计像素级运动量;然后,在经过粗配准的两幅图像中心选取尺寸相同的区域,进行快速傅里叶变换,在频域采用扩展的相位相关算法对图像进行精确配准。该方法利用图像的功率谱信息,减少对图像内容的依赖,运用基于最小二乘的曲面拟合法,实现亚像素图像配准参数估计,具有从粗到精的特点,有效提高了图像检测精度。文中最后对样本图像进行了图像配准对比实验,结果表明,该方法可以检测0.01 pixel的运动量,最大配准误差为0.004 8 pixel。     

数字图像相关技术的综合算法及其在断裂力学中的应用

鉴于散斑图的随机噪声和相关搜索运算过程仍是当今影响相关图像技术所获结果精度与计算速度两大有待研究解决的主要问题,提出一种基于小波变换、序惯相似度检测和统计相关算法三者相结合的新算法。其基本原理是应用小波变换对变形前后的散斑图进行滤波平滑处理;利用序惯相似度检测算法进行粗搜索,找到可能的匹配点;在可能的匹配点应用统计相关法进行细搜索,最终找到匹配点的位置。基本实验、计算和应用表明,这种算法在消除噪声和提高运算速度等方面,取得了良好的效果。     

2000年—2010年黄淮海平原夏玉米种植时空变化检测

用统计分析与遥感分类技术相结合的方法,进行了较高精度的夏玉米分类。在验证分类结果具有可靠质量的基础上,将分类影像二值化,采用图像叠合法得到了黄淮海平原2000年—2010年夏玉米变化检测图像,并按区域分析了该区域11年间夏玉米的种植变化情况。结果表明,2000年—2010年间,黄淮海平原夏玉米种植面积总体明显增加,其中南部区域种植面积逐年稳步增加,北部、东部和中部区域在2000年—2003年间为减少态势,在2003年—2010年间则持续增加;四大区域中,北部区域变化幅度最大;2000年—2010年间,黄淮海平原中、南部夏玉米种植比例不断加大,整个区域种植比重呈南移趋势。研究结果可为指导农业生产提供依据,具有较高的实用价值和推广性。     

基于邻域分割的空谱联合稀疏表示高光谱图像分类技术研究

传统的高光谱遥感影像分类算法侧重于光谱信息的应用。随着高光谱遥感影像的空间分辨率的增加,高光谱影像中相同类别的地物在空间分布上呈现聚类特性,将空间特性有效地应用于高光谱遥感影像分类算法对分类精度的提升非常关键。但是,高光谱影像的高分辨率提供空间聚类特性的同时,在不同地物边缘处表现出的差异性更加明显,若不对空间邻域像素进行甄选,直接将邻域光谱信息引入,设计空谱联合稀疏表示进行图像分割,则分类误差较大,收敛速度大大降低。将光谱角引入空谱联合稀疏表示图像分类理论中,提出了一种基于邻域分割的空谱联合稀疏表示分类算法。该算法利用光谱角计算相邻像素的空间相似度,剥离相似度较低的邻域像素,将相似度高的邻域像素定义为同类地物,引入空谱联合稀疏表示模型中,采用子联合空间追踪算子和联合正交匹配追踪算子对其优化求解,以最小重构误差为准则进行分类。选取AVIRIS及ROSIS典型光谱影像数据进行实验仿真,从中可以看出,随着光谱角分割阈值的提高,复杂的高光谱影像分类精度和平滑区域的高光谱影像分类精度均逐步提高,表明邻域分割在空谱联合稀疏表示分类中的必要性。     

Magnetic resonance image enhancement using highly sparse input

Lately, the Magnetic Resonance scans have struggled with its own inherent limitations, such as spatial resolution as well as long examination times. In this paper, a novel, rapid compressively-sensed magnetic resonance high resolution image resolution algorithm is presented. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a discrete dense displacement sampling for the deformable human body and motion analysis. The clinical trials as well as phantom based studied have been conducted. It has been proven that the proposed algorithm is able to enhance image spatial resolution, reduce motion artefacts and scan times.     

基于Shearlet变换的自适应图像融合算法

针对多聚焦图像与多光谱和全色图像的成像特点,结合Shearlet变换具有较好的稀疏表示图像特征的性质,提出了一种新的图像融合规则.并基于此融合规则,提出了基于Shearlet变换的自适应图像融合算法.在多聚焦图像的融合算法中,分别对聚焦不同的图像进行Shearlet变换,并基于本文提出的融合规则,对分解后的高低频系数进行融合处理. 通过与多种算法的比较实验证明了本文提出的算法融合的图像具有更高的清晰度和更加丰富的细节信息.在多光谱和全色图像的融合处理中,提出了一种基于Shearlet变换与HSV变换相结合的图像融合方法.该算法首先对多光谱图像作HSV变换,将得到的V分量与全色图像进行Shearlet分解与融合,在融合过程中对分解系数选用特定的融合准则进行融合,最后将融合生成新的分量与H、S分量进行HSV逆变换产生新的RGB融合图像. 该算法在空间分辨率和光谱特性两方面达到了良好的平衡,融合后的图像在减少光谱失真的同时,有效增强了空间分辨率. 仿真实验证明,本文算法融合的图像与传统的多光谱和全色图像融合算法相比,具有更佳的融合性能和视觉效果.     

Efficient anisotropic filtering of diffusion tensor images

To improve the accuracy of structural and architectural characterization of living tissue with diffusion tensor imaging, an efficient smoothing algorithm is presented for reducing noise in diffusion tensor images. The algorithm is based on anisotropic diffusion filtering, which allows both image detail preservation and noise reduction. However, traditional numerical schemes for anisotropic filtering have the drawback of inefficiency and inaccuracy due to their poor stability and first order time accuracy. To address this, an unconditionally stable and second order time accuracy semi-implicit Craig-Sneyd scheme is adapted in our anisotropic filtering. By using large step size, unconditional stability allows this scheme to take much fewer iterations and thus less computation time than the explicit scheme to achieve a certain degree of smoothing. Second-order time accuracy makes the algorithm reduce noise more effectively than a first order scheme with the same total iteration time. Both the efficiency and effectiveness are quantitatively evaluated based on synthetic and in vivo human brain diffusion tensor images, and these tests demonstrate that our algorithm is an efficient and effective tool for denoising diffusion tensor images.     

光谱特征分析的城市道路提取

道路是城市中典型的人造地物。利用高分辨率影像进行城市道路提取,对城市规划、交通发展具有重要意义。由于地物光谱的混淆性和异质性,利用传统基于光谱的分类方法很难将道路与其他城市地物区分开。针对这一问题,提出了一种利用道路边缘结构信息进行分类的方法,边缘作为光谱衍生信息对线性地物(如,道路等)识别具有明显的意义。首先,根据全色光谱波段纹理信息,利用改进的自适应Mean Shift算法进行边缘检测,最大限度减少噪声与伪边缘;然后,对边缘图像中的线段进行编组,利用统计模型依次对边缘线段求取统计特征,并将该统计特征与多光谱特征结合作为总分类特征;最后,利用监督学习方法对城市道路样本进行学习并对整个实验区域进行分类。结果表明将光谱信息与边缘统计特征融合对道路的识别精度为93%,相比传统方法78%的精度有显著的提高,因此,该方法是一种有效、可行的高分辨率遥感图像城市道路提取方法。     

An improved image restoration approach using adaptive local constraint

How to restore images well with reducing ringing and noise remains difficult. An improved image restoration algorithm based on adaptive local constraint is proposed. We consider the statistical property that first order of derivatives of image obeying Gaussian distributions to control the noise. In order to suppress the artifacts around regions where gray values vary severely, spatial weighted matrix is adopted which can impose a local constraint on restoration and smoothing. According to experiments of simulation and real images, this approach can well restore edge regions well as well as reduce the noise in smooth area, which means that the approach does validly in suppressing ringing ripples and preserve more details.     

基于成像系统物理特性的多光谱图像与全色波段图像融合

提出了一种基于成像系统物理特性的多光谱图像与全色波段图像融合算法。该算法采用àtrous小波变换提取全色波段图像的空间细节信息,并将提取的空间信息按照一定的注入模型调整后添加到各波段多光谱图像中去,得到具有高空间分辨力的多光谱图像。注入模型充分考虑了各波段成像传感器的相对光谱响应函数、地表物体对各波段的光谱反射率以及各波段的辐射调整系数等成像系统的物理特性,使融合后的多光谱图像在显著提高空间质量的同时,最大可能地保留了原始多光谱图像的光谱特性。对IKONOS卫星遥感影像的融合实验结果表明,该算法在光谱保留和空间质量提高方面较其它基于小波变换的融合算法都具有更高的性能。     

农产品品质光谱成像的空间预测规律

由于受到化学检测手段限制,无法获取光谱图像中的农产品品质在各像素位置处的参考值,因此无法直接验证基于光谱图像得到的农产品品质空间预测结果。为探索基于光谱图像的农产品空间品质检测规律,本文采用计算机生成已知空间品质样本,并分别以固定曝光和变曝光方式采集不同灰度等级标准板的光谱图像。定量分析采集系统误差,借助样本区域光谱与区域指标之间的全波段偏最小二乘（ALL-PLS）和遗传特征波长偏最小二乘（GA-PLS）预测函数,研究出区域指标预测准确时样本空间品质指标的预测精度规律,建立光谱成像空间预测准确度模型。实验结果表明：变曝光方式下的数据采集可以提高光谱图像信噪比,在波段两侧极限处尤为明显;应用区域品质数据预测空间品质分布,空间预测误差主要受光谱图像采集噪声影响：即使区域预测准确,空间预测可能已完全失真。通过衡量数据采集系统误差,可以间接评价农产品品质空间预测的准确度。只有在数据采集系统误差在允许范围内时,光谱成像技术才可准确预测农产品品质的空间分布。     

高功率激光器自动准直系统图像处理的可信度评估

自动准直系统是将采集的图像进行特定算法处理获取光束的位置信息,驱动电机调整光斑到指定的位置,其中图像质量对光束定位的精确性影响很大,为尽量避免受严重噪音和光束畸变影响的图像进入图像处理流程而产生准直结果的错误判断,保证自动准直系统图像处理结果的精确性,试图提供一种较为简单的判断依据,采用基于Monte-Carlo模拟方法,建立了远场准直过程中三种主要噪音的干扰评估模型,以测量不确定度作为定量判断处理图像与否的特征参数,将该结果做成统计图表作为判断依据。结果表明,在设定合理测量不确定度阈值的情况下,该特征参数能够较好地排除不良图像,从而提高准直结果的可信度。     

基于空间密度聚类的改进KRX高光谱异常检测

高光谱遥感影像包含了丰富的光谱信息,对于地物具有极强的分辨能力,从而促进了不需任何先验信息的高光谱异常目标探测技术的发展。KRX（Kernel RX）异常探测算法巧妙地利用核函数将RX算法映射至高维特征空间,加强了光谱中非线性信息的运用,具有较强的可分辨性,显著改善了低维空间的光谱不可分问题。然而,也暴露了KRX算法中病态Gram矩阵求逆误差大,异常检测效率低等缺点。为实现理论上KRX算法的强探测性能,提出一种基于新型聚类方法的改进KRX探测算法（DC-KRX）。（1）由于空间邻域像元具有较强的光谱相似性,会造成Gram矩阵病态,严重影响了异常探测效果,因此背景虚检现象严重。针对病态Gram矩阵的求逆误差问题,算法改进了KRX算子,对Gram矩阵进行奇异值分解,选取特征值较大的主成分,保证了Gram矩阵的求逆精度,待测像元的探测结果采用l-2范数表示,检测效果提高明显;（2）在改进KRX的基础上,提出了空间聚类KRX算法。空间像元之间具有光谱强相关性,既造成了Gram矩阵的病态,数据的冗余也影响了探测效率。实验发现,通过聚类算法可以合并像元于聚类中心,减少空间维度,提高计算效率;同时,聚类中心按照聚类大小被赋予不同的权重,保证了探测精度;（3）另一方面,选用合适的聚类算法是一个难点。聚类KRX算法对于聚类算法的精度和实时性要求较高,比较发现,一种基于密度峰值快速搜索（DC）的新型聚类算法具有较好的聚类性能。算法采用欧式距离计算任意像元的相似度,利用局部密度和邻域距离作为聚类中心的联合判断准则,对结果进行排序得到聚类中心。实验发现,该聚类算法计算速度快,且能够对任意形状的分布进行聚类,非常适合于维度较高,成分复杂的高光谱图像,且适用于较高次数的重复聚类。DC-KRX算法提供了一种空间聚类预处理的高光谱异常探测新思路,最后,与国际主流探测算法对比发现,该算法表现了较好的探测性能。同时,时效性对比分析发现,聚类前后算法的检测效率提高了30%以上,有效改善了KRX算法的实时性。     

基于Powell算法的亚像元配准方法

基于Powell优化算法和图像的金字塔分解,提出了一个新的亚像元运动参数估计方法.用参考图像和待配准图像构造的非线性最小二乘公式作为Powell算法的目标函数,并且以运动参数作为变量.由细到粗的多分辨率分解为准确的亚像元参数估计提供了基础.数值实验结果表明,该算法可以用于准确的稳健的亚像元参数估计,其不足之处在于运行时间比较长.     

A comparison of iterative algorithms and a mixed approach for in-line X-ray phase retrieval

Previous studies have shown that iterative in-line X-ray phase retrieval algorithms may have higher precision than direct retrieval algorithms. This communication compares three iterative phase retrieval algorithms in terms of accuracy and efficiency using computer simulations. We found the Fourier transformation based algorithm (FT) is of the fastest convergence, while the Poisson-solver based algorithm (PS) has higher precision. The traditional Gerchberg-Saxton algorithm (GS) is very slow and sometimes does not converge in our tests. Then a mixed FT-PS algorithm is presented to achieve both high efficiency and high accuracy. The mixed algorithm is tested using simulated images with different noise level and experimentally obtained images of a piece of chicken breast muscle.