基于改进邻域均值的全色影像水域提取

利用传统邻域均值法对全色影像水域进行提取时,所提取的水域边界精确度严重依赖于搜索窗口的大小。本文通过分析水域灰度特征以及提取边界不精确的原因,对方法进行针对性的改进:依据邻域各像素点与种子点的灰度差值分别对其赋予不同的灰度值,以处理后的邻域均值作为特征进行水域提取。实验结果表明:基于改进的邻域均值方法对遥感影像水域有较好的提取结果,提取精度较之改进前有明显改善。     

多代卫星测高数据联合平差及重力场反演

为了解决多代卫星测高数据之间的不协调问题,基于误差验后补偿理论,提出了将传统的交叉点平差整体解法简化为两步处理法,即首先使用条件平差法对交叉点观测方程进行平差计算,然后沿测高轨迹进行海面高滤波和推估。使用新方法可大大简化联合平差的计算过程,且有利于提高计算结果的稳定性和可靠性,对规则和不规则的区域网和全球网交叉点平差问题都同样适用。实际算例证明了新方法的有效性。     

DPS在变形监测数据处理中的应用

介绍应用DPS软件处理变形监测数据的优点,并应用该软件,结合时间序列分析理论,对某一实测数据进行处理,结果表明,处理结果能达到令人满意的效果.     

Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data

Abstract

This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron (MLP) in the Zagros Folded Belt, Iran, and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact assessment. MLP neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data, and the results were compared using confusion matrices. The most appropriate classification image for L1B input to MLP was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%. The MLP result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs, respectively. Field observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified remotely. It is concluded that MLP is an efficient approach for mapping salt diapirs and salt-affected areas.     

陆地重力精化技术软件平台的设计与实现

陆地重力精化技术是精确重力场信息、提供重力场应用保障的关键技术手段.介绍了陆地重力精化技术软件平台的设计思想和主要功能并对各部分涉及的关键技术进行了分析与阐述.软件平台由重力场数据处理、重力场数据应用与分析以及重力场数据可视化3部分组成.实现了重力观测数据的数据库管理,重力场数据处理的自动化,野外重力测量布点仿真和重力场信息的图形可视化等功能.     

多频多模导航卫星系统的组合研究

随着中欧伽利略计划的实施，多频多模导航卫星系统的综合应用是未来卫星导航系统的发展方向。在对GPS、GLONASS和GALILEO三个系统各自的特点及其未来发展进行分析的基础上，从其信号频率，卫星可见性，可见稳定性，覆盖范围及数据处理结果等层面研究了三个卫星导航系统的最佳组合方式，并讨论了多系统组合的初步数据处理方法。     

Bayesian and geostatistical approaches to combining categorical data derived from visual and digital processing of remotely sensed images

This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique.     

高空资料质量问题的原因分析

分析了山东省高空资料审核中出现的问题及原因,并对特殊情况和异常数据提出了处理办法.     

自动气象站缺测数据分析及处理

地面自动观测系统正逐步替代传统的人工气象观测,自动气象站容易受到直接雷击及雷电电磁脉冲的侵害、仪器性能下降、电磁干扰和人为操作失误等因素影响,导致自动站采集器等设备损坏,造成业务中断,数据缺测,影响了气象数据的及时性、完整性、连续性。因此,台站观测人员需要在自动站设备故障,资料不正常时及时采取有效措施,对数据进行补测、挽救,保证气象数据完整、按时上传。对自动气象站资料缺测的原因进行初步分析,并着重对自动站定时观测、每小时上传文件及日、月数据极值、统计缺测等方面的处理进行探讨。     

Porting of a wildfire risk and fire spread application into a cloud computing environment

Effective wildfire management is an essential part of forest firefighting strategies to minimize damage to land resources and loss of human lives. Wildfire management tools often require a large number of computing resources at a specific time. Such computing resources are not affordable to local fire agencies because of the extreme upfront costs on hardware and software. The emerging cloud computing technology can be a cost- and result-effective alternative. The purpose of this paper is to present the development and the implementation of a state-of-the-art application running in cloud computing, composed of a wildfire risk and a wildfire spread simulation service. The two above applications are delivered within a web-based interactive platform to the fire management agencies as Software as a Service (SaaS). The wildfire risk service calculates and provides daily to the end-user maps of the hourly forecasted fire risk for the next 112 hours in high spatiotemporal resolution, based on forecasted meteorological data. In addition, actual fire risk is calculated hourly, based on meteorological conditions provided by remote automatic weather stations. Regarding the wildfire behavior simulation service, end users can simulate the fire spread by simply providing the ignition point and the projected duration of the fire, based on the HFire algorithm. The efficiency of the proposed solution is based on the flexibility to scale up or down the number of computing nodes needed for the requested processing. In this context, end users will be charged only for their consumed processing time and only during the actual wildfire confrontation period. The system utilizes both commercial and open source cloud resources. The current prototype is applied in the study area of Lesvos Island, Greece, but its flexibility enables expansion in different geographical areas.