Strain fields and mechanical response of a highly to medium fissured bentonite clay

This article presents the developments of an ongoing research aimed at modelling the influence of fissuring on the behaviour of clays. In particular, it recalls the main results of an extensive laboratory investigation on a fissured bentonite clay from the south of Italy and presents the data of a new investigation on the evolution with shearing of the strain fields developing within the clay, resulting from Digital Image Correlation (DIC). Element test results are analysed in the framework of continuum mechanics and linked to the clay fissuring features, once characterised using the Fissuring IDentity (F‐ID) chart. This article compares the bentonite behaviour with that of other fissured clays of different F‐IDs, highlighting the common behavioural features. Thereafter, the soil response at the macro level is related to the DIC‐derived strain fields evolving within the clay with loading. For this purpose, DIC was successfully used to investigate the deformation processes active in the fissured clay and the sources of the localisation phenomena. DIC is shown to provide indications of the extent to which highly to medium fissured clays element test results can be of use to model the clay behaviour according to continuum mechanics. Copyright © 2012 John Wiley & Sons, Ltd.     

太阳目标哈特曼波前探测研究

分别对Hinode光学望远镜SOT观测太阳黑子的本影和半影图像以及太阳米粒组织图像进行了空间频谱计算和相关计算,分析了太阳不同结构区域图像的时域和空域特性对哈特曼波前探测精度的影响。计算和分析结果表明,在子孔径波面倾斜小于0.25″情况下,米粒组织图像采样时间在2 min内、本影和半影采样时间在4 min内,对子孔径波面倾斜探测精度的影响极小;并且由太阳目标特性引起的哈特曼波面探测误差随波面畸变量的增大而增大。这些研究结果可为太阳望远镜哈特曼波前探测器研制和应用提供依据和参考。     

基于CCD星图的空间目标精确光学定位方法研究

通过光学定位方法来确定空间目标的轨道是实现空间目标监测的重要手段之一。介绍一套小型的空间目标光学定位系统,针对该系统获取的CCD星图,提出Top-hat变换和中值滤波相结合的算法去除星图噪声,并提出基于梯度Hough变换的算法完成对CCD星图星点和轨迹的同时精确定位,最后利用星角距的旋转不变性和球面几何原理,实现对空间目标的精确定位。对分辨率为752×580的CCD实拍星图的应用研究结果表明,星图去噪算法和星点、轨迹同时精确定位算法是有效的,且空间目标的定位精度也可达到角秒量级。     

Super-resolution of THEMIS thermal infrared data: Compositional relationships of surface units below the 100 meter scale on Mars

The Thermal Emission Imaging System (THEMIS) has provided the highest spatial resolution (100 m/pixel) thermal infrared (TIR) data of the surface of Mars to date. These data have enabled the discovery of many small-scale compositional units and helped to better constrain surface processes operating at these scales. However, with higher-resolution visible (VIS) instruments revealing smaller-scale surficial differences, there exists a need to detect and map compositional variability using TIR data at scales below 100 m. Because it is unlikely there will be a higher-resolution TIR instrument sent to Mars in the near future, creative image processing techniques commonly classified under the umbrella of “super-resolution” can be employed to improve or enhance the spatial resolution of the THEMIS TIR data. These approaches typically integrate another higher-resolution dataset and can either be qualitative for visual appeal, quantitative for data accuracy, or some combination of both. The super-resolution approach presented here produces enhanced TIR images that are radiometrically accurate, but also visually appealing. For the technique to be successfully applied, multi- to hyper-spectral data from two different spectral regions are required (e.g., the THEMIS TIR and VIS datasets). The focus here is to introduce this new super-resolution methodology and demonstrate its ability using existing THEMIS IR and VIS data. The quartzofeldspathic deposit in northern Syrtis Major was selected because of the spectral variability detected using the original IR resolution data and to better constrain the relationship between the small-scale surface morphology and areal extent of the deposit as well as its formation process. Despite being associated with the central peaks of two craters, the results here show no positive correlation between the small rocky outcrops and the quartzofeldspathic unit. A gradational contact exists between the unit and basaltic sands within the intercrater eolian material. The super-resolution approach offers an alternative approach to traditional sub-pixel deconvolution identification and provides a higher-resolution IR dataset for thermophysical and spectral analysis on Mars.     

基于图像技术的胶州湾浮游动物优势种体型参数与生物量转换关系研究

利用ZooScan图像扫描技术,建立胶州湾浮游动物优势种中华哲水蚤和强壮箭虫体长、面积和体积与干重之间的回归关系.比较了不同季节、不同参数之间的差异.结果表明,不同月份的回归关系之间无显著差异,在三个体型参数中,面积拟合生物量的程度最好,是利用图像方法估算生物量最佳参数.中华哲水蚤和强壮箭虫体型-生物量转换关系可以分别表示为:lgDW=1.611gA+ 1.69,lgDW=1.541gA+l.32.     

基于图像测速的波浪破碎两相速度场测量方案探讨

针对波浪破碎试验中气泡区速度与水体速度无法同时测量的难题,基于实验室条件,搭建了粒子图像测速（PIV）和气泡图像测速（BIV）系统,采用声学多普勒测速仪（ADV）对PIV测量精度和不同场景下BIV测量精度进行了细致的验证,并设计多种试验方案以期实现BIV与PIV的同步耦合测量。试验结果表明,7W激光+后置灯的优化照明方案,其耦合测量的气泡区速度代表参数与标准BIV照明方案的结果误差小于5%,且耦合测量的气泡区下方水质点的速度曲线与标准PIV测量结果吻合,表明该改进方案满足PIV与BIV耦合测量的条件。本文实现的PIV与BIV耦合测量有利于优化测量和分析工作,减小破碎波浪重复生成的影响,有利于提高测量精度,为大范围波浪破碎速度场的快速测量提供了新的方案。     

A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation

High spatial resolution and spectral fidelity are basic standards for evaluating an image fusion algorithm. Numerous fusion methods for remote sensing images have been developed. Some of these methods are based on the intensity–hue–saturation (IHS) transform and the generalized IHS (GIHS), which may cause serious spectral distortion. Spectral distortion in the GIHS is proven to result from changes in saturation during fusion. Therefore, reducing such changes can achieve high spectral fidelity. A GIHS-based spectral preservation fusion method that can theoretically reduce spectral distortion is proposed in this study. The proposed algorithm consists of two steps. The first step is spectral modulation (SM), which uses the Gaussian function to extract spatial details and conduct SM of multispectral (MS) images. This method yields a desirable visual effect without requiring histogram matching between the panchromatic image and the intensity of the MS image. The second step uses the Gaussian convolution function to restore lost edge details during SM. The proposed method is proven effective and shown to provide better results compared with other GIHS-based methods.     

基于ArcGIS镶嵌数据集的正射影像数据库构建方法初探

数字正射影像数据具有直观、信息量大、覆盖面广和经济、方便、获取快速等优点,是我国基础地理信息数据库的重要内容。本文讨论了基于ArcGIS镶嵌数据集栅格模型构建数字正射影像数据库的一些基本问题。首先对近年来的影像空间数据库技术进行了梳理,然后基于ArcGIS镶嵌数据集模型提出了数字正射影像数据库构建方法,其中对影像入库涉及的栅格数据存储模型、建库步骤、相关参数等问题进行了分析与探讨。最后,开展了海南省某测区小型正射影像数据库构建试验,通过试验证明了文中方法的可用性与可靠性。     

一种大文件多版本遥感影像数据组织管理方法

近年来,遥感技术的飞速发展使得影像数据的使用也越来越广泛和深入,而影像分辨率的增加,使得数据量急剧增长,对数据的存储和管理提出了很高的要求。针对传统遥感影像数据组织存储的不足,本文提出了一种大文件金字塔数据存储结构,用来高效存储管理影像数据;同时针对多时相遥感影像数据,提出了基于版本机制的影像数据管理模式。实验表明这种方法具有更高的存储检索效率。     

An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery

We tested the effects of three fast pansharpening methods – Intensity-Hue-Saturation (IHS), Brovey Transform (BT), and Additive Wavelet Transform (AWT) – on sugarcane classification in a Landsat 8 image (bands 1–7), and proposed two ensemble pansharpening approaches (band stacking and band averaging) which combine the pixel-level information of multiple pansharpened images for classification. To test the proposed ensemble pansharpening approaches, we classified “sugarcane” and “other” land cover in the unsharpened Landsat multispectral image, the individual pansharpened images, and the band-stacked and band-averaged ensemble images using Support Vector Machines (SVM), and assessed the classification accuracy of each image. Of the individual pansharpened images, the AWT image achieved higher classification accuracy than the unsharpened image, while the IHS and BT images did not. The band-stacked ensemble images achieved higher classification accuracies than the unsharpened and individual pansharpened images, with the IHS-BT-AWT band-stacked image producing the most accurate classification result, followed by the IHS-BT band-stacked image. The ensemble images containing averaged pixel values from multiple pansharpened images achieved lower classification accuracies than the band-stacked ensemble images, but most still had higher accuracies than the unsharpened and individual pansharpened results. Our results indicate that ensemble pansharpening approaches have the potential to increase classification accuracy, at least for relatively simple classification tasks. Based on the results of the study, we recommend further investigation of ensemble pansharpening for image analysis (e.g. classification and regression tasks) in agricultural and non-agricultural environments.