H-alpha太阳全日面像数据管理系统的建立

云南天文台的H-alpha太阳色球望远镜每天的数据量约有2G bytes，大量数据带来了使用上的不便。因此，实现数据的查询和共享成为了一个相当重要的问题。为此，试图通过建立H-alpha数据的管理系统，实现对工作流程的改进，完成对数据的检索、在线浏览和远程下载。在WindwsXP操作系统和MysQL数据库管理系统的平台上，利用JAVA语言和JSP技术开发了一个基于浏览器／服务器模式的原型系统，可以完成对数据的压缩存储，实现数据的检索、实时浏览、删除(需要相应权限)和下载等功能。     

Image matching as a data source for forest inventory – Comparison of Semi-Global Matching and Next-Generation Automatic Terrain Extraction algorithms in a typical managed boreal forest environment

Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method.Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant.     

Determination of the cleat angle distribution of the RECOPOL coal seams, using CT-scans and image analysis on drilling cuttings and coal blocks

Cleat orientation, cleat frequency and cleat angle distribution of deep coal seams are only available by the use of drilling cores and from coal mine samples. Coal drilling cuttings are a cheap and fast alternative to measure cleat angle distributions with the use of image analysis techniques. In this study oriented coal samples and drilling cuttings of the RECOPOL field experiment are compared and used to explain and validate the proposed method. In other words, cleat angle distributions from drilling cuttings are measured by image analysis. The geological framework of the polish coals is described. The image analysis methodologies for the measurement of fracture faces of cuttings and from CT-scan images, derived from these coals, are explained. The results of the methods on the cuttings are compared with cleat orientation distributions from CT-scans and artificial fragments from coal blocks of the same seams. These evaluations show high agreements between the methods. The cleat angle distributions of drilling cuttings of four seams are compared with the cleat orientation distributions of a regional structural geological study. The high correlation in this study shows that cleat angle distributions of coal seams can be used as input parameters for reservoir modelling.     

基于OpenGL的3维虚拟林相研究

本文介绍了研发虚拟林相三维可视化系统的技术流程与实现方法,该系统研发基于目前较流行的以Visual StudioC＋＋开发组件调用OpenGL三维图形库的方法,自主研发出虚拟林相三维可视化系统。该系统按三角带组织DEM数据并读入VC,采用LOD、四叉树技术自主研发了标准真实DEM数据构建三维地形的导入引擎,实现了地形可视化以及一些基本功能。为还原真实的地形地貌．应用OpenGL中的纹理映射、光照、场景分割、反走样、八叉树、雾化等三维可视化特性,增强了三维可视化效果。     

A MODIS-based automated flood monitoring system for southeast asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017).The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-h latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.     

对比剂不同注射方案对冠状动脉CTA图像质量的影响

目的 分析冠状动脉CTA扫描时对比剂的不同注射方案对其图像影响,寻求最佳注射方案。方法 选取240例体重相近患者随机平均分成A、B、C 3组行冠状动脉CTA扫描。A组应用双期注射方案,首次注入70ml对比剂,随后追加20ml生理盐水;B组应用双期注射方案,首次注入50ml对比剂,随后追加20ml混合剂（盐水∶对比剂为1∶1）,C组应用3期注射方案,首次注入50ml对比剂,第2期注入20ml混合剂（盐水∶对比剂为1∶1）,随后追加生理盐水20ml。评价3组CTA图像升主动脉（AO）及冠状动脉三大分支CT值比较,差异有无统计学意义,比较3组间上腔静脉、右心室硬化伪影及室间隔显示情况。结果 与A组比较,B、C两组在减少10ml对比剂情况下,3组间AO及冠状动脉三大分支CT值比较,差异无统计学意义（P>0.05）,B组上腔静脉、右心室硬化伪影高于A、C两组（P<0.05）,C组室间隔显示率高于A、B两组（P<0.05）。结论 应用第3时相对比剂注射方案,既能有效减少造影剂用量,又能保证图像质量,较好地显示室间隔,对冠状动脉疾病的诊断具有一定意义。     

Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites

Pixel-based image compositing enables production of large-area surface reflectance images that are largely devoid of clouds, cloud shadows, or haze. Change detection with spectral trend analysis uses a dense time series of images, such as pixel-based composites, to quantify the year, amount, and magnitude of landscape changes. Topographically-related shadows found in mountainous terrain may confound trend-based forest change detection approaches. In this study, we evaluate the impact of topographic correction on trend-based forest change detection outcomes by comparing the amount and location of changes identified on an image composite with and without a topographic correction. Moreover, we evaluated two different approaches to topographic correction that are relevant to pixel-based image composites: the first corrects each pixel according to the day of year (DOY) the pixel was acquired, whilst the second corrects all pixels to a single reference date (August 1st), which was also the target date for generating the pixel-based image composite. Our results indicate that a greater area of change is detected when no topographic correction is applied to the image composite, however, the difference in change area detected between no correction and either the DOY or the August 1st correction is minor and less than 1% (0.54–0.85%). The spatial correspondence of these different approaches is 96.2% for the DOY correction and 97.7% for the August 1st correction. The largest differences between the correction processes occur in valleys (0.71–1.14%), upper slopes (0.71–1.09%), and ridges (0.73–1.09%). While additional tests under different conditions and in other environments are encouraged, our results indicate that topographic correction may not be justified in change detection routines computing spectral trends from pixel-based composites.     

Fusion of HJ1B and ALOS PALSAR data for land cover classification using machine learning methods

Image classification from remote sensing is becoming increasingly urgent for monitoring environmental changes. Exploring effective algorithms to increase classification accuracy is critical. This paper explores the use of multispectral HJ1B and ALOS (Advanced Land Observing Satellite) PALSAR L-band (Phased Array type L-band Synthetic Aperture Radar) for land cover classification using learning-based algorithms. Pixel-based and object-based image analysis approaches for classifying HJ1B data and the HJ1B and ALOS/PALSAR fused-images were compared using two machine learning algorithms, support vector machine (SVM) and random forest (RF), to test which algorithm can achieve the best classification accuracy in arid and semiarid regions. The overall accuracies of the pixel-based (Fused data: 79.0%; HJ1B data: 81.46%) and object-based classifications (Fused data: 80.0%; HJ1B data: 76.9%) were relatively close when using the SVM classifier. The pixel-based classification achieved a high overall accuracy (85.5%) using the RF algorithm for classifying the fused data, whereas the RF classifier using the object-based image analysis produced a lower overall accuracy (70.2%). The study demonstrates that the pixel-based classification utilized fewer variables and performed relatively better than the object-based classification using HJ1B imagery and the fused data. Generally, the integration of the HJ1B and ALOS/PALSAR imagery can improve the overall accuracy of 5.7% using the pixel-based image analysis and RF classifier.     

In-flight calibration of the Cassini imaging science sub-system cameras

We describe in-flight calibration of the Cassini Imaging Science Sub-system narrow- and wide-angle cameras using data from 2004 to 2009. We report on the photometric performance of the cameras including the use of polarization filters, point spread functions over a dynamic range greater than 10⁷, gain and loss of hot pixels, changes in flat fields, and an analysis of charge transfer efficiency. Hot pixel behavior is more complicated than can be understood by a process of activation by cosmic ray damage and deactivation by annealing. Point spread function (PSF) analysis revealed a ghost feature associated with the narrow-angle camera Green filter. More generally, the observed PSFs do not fall off with distance as rapidly as expected if diffraction were the primary contributor. Stray light produces significant signal far from the center of the PSF. Our photometric analysis made use of calibrated spectra from eighteen stars and the spectral shape of the satellite Enceladus. The analysis revealed a shutter offset that differed from pre-launch calibration. It affects the shortest exposures. Star photometry results are reproducible to a few percent in most filters. No degradation in charge transfer efficiency has been detected although uncertainties are large. The results of this work have been digitally archived and incorporated into our calibration software CISSCAL available online.     

ePSF拟合法与Gaussian拟合法的比较

为了解决哈勃空间望远镜的图像欠采样问题,美国学者Anderson和King提出了精确测量星像位置和通量的有效点扩散函数(ePSF—effective point-spread function)拟合法。然而,他们却不加比较地将其应用于地面CCD图像中星像的位置高精度测量。因此,我们试图将ePSF拟合法与经典的高斯函数(Gaussian)拟合法作比较研究。调用CFITSIO库生成模拟的背景图像,并应用不同参数条件下的星像轮廓模型产生非欠采样的星像。最后,分别采用ePSF拟合法与Gaussian拟合法对这些星像进行拟合,并对它们的拟合精度进行比较。实验结果表明,在非欠采样的图像中这两种算法对星像位置的测量几乎是等精度的。