一种空间域矢量地图数据盲水印算法

对目前已有的针对二维矢量数据的水印算法进行了分析,并提出了一种新的鲁棒的盲水印算法。该算法首先将矢量数据分块,然后在不同分块中,结合密钥修改某些点的x坐标或者y坐标,从而嵌入水印信息。实验证明,该算法对平移、坐标转换、制图综合、插值等常规操作以及地物对象顺序置乱等攻击具有较好的鲁棒性,并且是一种盲检算法,具有较好的实用价值。     

S形分布的数据拟合数学模型研究

S形曲线有着广泛的应用,以往的研究模型仅含三个参数,大多以对称S形分布的基本光滑数据为例,这使得自由度受限的和以理想数据为研究对象所得到的研究结果难以适用于更实际的、非对称(左偏S形分布、右偏S形分布)的非光滑观测数据。为此,提出并建立了含有5个参数的二次有理模型,作为对S形分布的观测数据进行拟合的模型,并进一步分为带导数拟合、5分段和值拟合和5特征点拟合,使S形分布的各种情况(左偏、对称和右偏)的拟合得到明显改善。     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。     

基于主成分分析的ENVISAT ASAR数据在福建省漳州市香蕉园地提取中的应用(英文)

Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multi-temporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier. Supported by the Program for New Century Excellent Talents in University (NCET-05-0573), Fujian Science and Technology Project (No.2006I0018), the Science Project of the Education Department of Fujian Province(No. 2006F5022).     

基于Geodatabase的基础地理空间数据库设计

介绍了基于Geodatabase的空间数据牢设计方法,设计了城市基础地理空间数据库,并实现了城市基础地理空间数据的整合与共享。     

大型油罐沉降监测数据处理软件的实现

介绍了针对某批大型油罐的沉降监测数据处理软件制作过程,采用了Excel电子表格软件在VBA可视化编程环境下完成,实现了工程测量数据的及时处理分析。     

Classifier ensembles for land cover mapping using multitemporal SAR imagery

SAR data are almost independent from weather conditions, and thus are well suited for mapping of seasonally changing variables such as land cover. In regard to recent and upcoming missions, multitemporal and multi-frequency approaches become even more attractive. In the present study, classifier ensembles (i.e., boosted decision tree and random forests) are applied to multi-temporal C-band SAR data, from different study sites and years. A detailed accuracy assessment shows that classifier ensembles, in particularly random forests, outperform standard approaches like a single decision tree and a conventional maximum likelihood classifier by more than 10% independently from the site and year. They reach up to almost 84% of overall accuracy in rural areas with large plots. Visual interpretation confirms the statistical accuracy assessment and reveals that also typical random noise is considerably reduced. In addition the results demonstrate that random forests are less sensitive to the number of training samples and perform well even with only a small number. Random forests are computationally highly efficient and are hence considered very well suited for land cover classifications of future multifrequency and multitemporal stacks of SAR imagery.     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

广域网络环境下的土地资源空间数据更新机制

在分析土地资源数据更新内部联动性较强等特点的基础上,研究了适合广域网络环境的土地资源数据更新机制。该土地资源数据更新机制采用选定工作区域、数据提取、数据下载、离线数据编辑、数据上传、数据更新等流程来具体实现。测试表明：该土地资源数据更新机制能较好地运用于县级土地资源管理部门,满足县、乡等多级土地资源管理的日常业务需求。     

应用于村镇信息自助平台的电子地图构件关键技术研究

从构件化村镇信息自助平台的低成本、易定制的特点出发,针对其中电子地图构件在地图浏览和交互查询中存在的网络负载大、响应速度慢等技术问题,研究了传统We—bGIS空间数据网络发布模式及其不足,探讨了基于AJAX、地图切片、多级缓存技术的电子地图发布解决方案,并实现了在村镇信息自助平台上的应用。