数字地形分析应用适配性知识的案例表达与推理方法

数字地形分析(Digital Terrain Analysis, DTA)在应用时依赖于建模知识,尤其是关于所建的应用模型是否与研究区特点、数据等条件相适配的知识(称为“应用适配性知识”);由于这类知识难以形式化表达,现有的数字地形分析工具对此类知识缺乏利用,从而导致普通用户在应用数字地形分析时建模困难。针对该问题,设计了一套数字地形分析领域应用适配性知识的案例表达与相应的推理方法。以美国32个河网提取案例为例,通过交叉验证,初步表明案例及其推理应用方法适合于数字地形分析领域应用适配性知识的形式化表达与应用,该方法通过与建模环境的集成,可大幅降低数字地形分析应用建模难度。     

面向电子地图应用的地理信息快速更新方法研究

针对电子地图应用中快速更新的问题,提出一种面向电子地图应用的地理信息快速更新的方法。以电子地图的应用为切入点,探讨电子地图快速更新的方式。提出了面向电子地图更新的五层架构,阐述了这一架构的基本流程、变化发现、框架库提取等关键技术。     

基于分类与形态综合的高分辨率影像建筑物提取方法研究

本文提出了一种基于分类和形态综合的高分辨率影像建筑物提取方法。主要研究的是基于高分辨率影像多尺度分割的面向对象人工地物分类方法,并以此为基础提取房屋,实现房屋位置、范围的初步自动化提取。最后,对提取的地物图斑的精确边界利用Hough变换提取直线边缘轮廓线,并以此为基础进行轮廓拟合和制图综合,从而得到规则的、方形组合的建筑物形状信息。     

基于潮汐动态淹没过程的长江口潮滩地形信息反演研究

潮滩是陆地与海洋之间重要的生态过渡地带,具有复杂的生态过程和重要的服务功能。受陆海交互作用及人类活动的影响,潮滩处于高度动态变化过程中,而传统测绘技术受到潮滩可达性影响无法快速获取潮滩地形信息。为解决潮滩高程数据获取困难的问题,本文提出一种基于潮汐动态淹没过程和时序遥感影像的潮滩地形信息提取算法,利用K-means++聚类方法实现水域提取,并通过时序淹没特征计算潮滩淹没频率提取潮滩范围信息,最终综合区域潮汐特征反演潮滩地形。研究以崇明东滩为例,利用2016—2020年所有可用Sentinel-2和Landsat-8时序遥感影像,实现潮滩范围提取与高程反演,并通过实测高程数据进行精度验证。研究结果表明,潮滩范围提取总体精度为97.73%,F1_score为0.98;高程反演平均绝对误差为0.15 m,潮滩高程的反演精度与可用影像的数量呈正相关。研究利用该算法进一步反演长江口地区主要潮滩地形特征,结果表明区域内潮滩面积为346.93 km²,高程范围为1.00~3.84 m,且与现有潮滩范围数据集相比,本研究提取的长江口潮滩范围更为完整。该算法为潮滩地形的快速反演提供了可能,对潮滩资源动态监测和管理具有重要意义。     

不同方法提取马氏珠母贝外套膜胶原蛋白理化性质比较

【目的】系统分析不同方法提取的马氏珠母贝(Pinctada martensii)外套膜胶原蛋白的理化性质及生物学功能。【方法】分别采用酸-酶法和热水法从外套膜中提取得到两种胶原蛋白(A-PSC(Pm)和HSC(Pm)),对其氨基酸组成、微观结构及热变性温度等理化特性进行比较分析。【结果】HSC(Pm)提取率高于A-PSC(Pm);HSC(Pm)中,Gly、Glu与Arg相对含量较高,分别为20.16%、17.57%和10.07%;而A-PSC(Pm)中,Glu、Asp与Arg相对含量为19.67%、11.63%和10.07%;紫外光谱和红外光谱分别显示,两种胶原蛋白均在220 nm左右有强吸收峰,且具有典型的胶原蛋白特征带(酰胺A、B、Ⅰ、Ⅱ和Ⅲ带);扫描电镜结果显示,A-PSC(Pm)呈致密多孔网状结构,HSC(Pm)则基本为薄片状;DSC分析结果表明,HSC(Pm)具有更好的热稳定性。【结论】酸-酶法及热水法提取的胶原蛋白均具有类V型胶原蛋白特性,其结构特征及其理化性质均存在差异,可根据需求选择特定的胶原蛋白提取方法用以开发不同类型的产品。     

北京地区重磁数据计算机图像处理及地质效果

介绍了对北京地区重力和磁力数据进行计算机图像处理的技术及流程,解译了重磁数据图像处理结果,分析了重磁数据计算机图像处理的地质效果,得到了一些对北京地区成矿预测有益的新认识     

Preliminary study on weathering and pedogenesis of carbonate rock

South China is the largest continuous distribution area of carbonate rock in the world. The origin of the soils over the bedrock carbonate rock has long been a controversial topic. Here further exploration is made by taking five soil profiles as examples, which are developed over the bedrock dolomitite and limestone and morphologically located in upland in karst terrain in the central, west and north Guizhou as well as west Hunan, and proved to be the weathering profiles of carbonate rock by the research results of acid-dissolved extraction experiment of bedrock, mineralogy and trace element geochemistry. Field, mineralogical and trace element geochemical characteristics of weathering and pedogenesis for carbonate rock are discussed in detail. It is pointed out that weathering and pedogenesis of carbonate rock are important pedogenetic mechanisms for soil resources in karst area, providing a basis for further researches on the origin of soils widely overlying bedrock carbonate rocks in South China. Project supported by the National Natural Science Foundation of China (Grant No. 498330003) and National Key Basic Research Project (Grant No. 95pre-39).     

离子色谱分析中的样品预处理

总结了几种常用于离子色谱分析的样品预处理方法,其中包括流动相改性,气化分离法,离子交换法,膜技术,溶剂萃取,超临界流体萃取,固相萃取以及柱前和柱后衍生。引用主要参考文献１０篇。     

Region of interest extraction via common salient feature analysis and feedback reinforcement strategy for remote sensing images

Saliency detection is an effective approach to extract regions of interest (ROIs) for remote sensing images. However, existing saliency detection models mainly focus on ROI extraction from a single image and usually are not able to produce satisfactory results because of complex background interference in remote sensing images. The employment of mutual information in a set of remote sensing images can provide an effective solution to this issue. In this paper, we propose a novel saliency detection model for multiple remote sensing images to simultaneously extract ROIs and identify images without ROIs. First, common salient feature analysis based on synthesized feature clustering and global contrast is implemented to exploit global correspondence in the synthesis feature domain, thereby highlighting preliminary ROIs against background interference and assigning lower saliency values to images without ROIs. Then, we design an exclusion criterion based on saliency value judgment to remove images without ROIs, and the remaining saliency maps are refined by an enhancement strategy. Finally, the enhanced maps serve as a feedback to yield a homogenous synthesized feature space in which integral ROIs with subtle borders are extracted by the reused cluster-based saliency calculation. Experiments reveal that our model outperforms seven state-of-the-art models by achieving the best ROC curve (AUC = 0.945) and maximal F-measure at 0.729.