山区遥感影像地形辐射统计校正模型改进研究

山区地形起伏导致太阳光照不同,造成遥感影像接收的辐射信息不能反映地表的真实信息,从而影响遥感影像的解译与处理.本研究基于常用的地形辐射统计校正模型,结合山区植被覆盖特征,设计了一种分区统计校正模型,并采用三种不同分辨率的卫星遥感影像进行了实验,结果显示改进后模型的地形辐射校正效果有明显改善.     

山区遥感影像地形辐射统计校正模型改进研究

山区地形起伏导致太阳光照不同,造成遥感影像接收的辐射信息不能反映地表的真实信息,从而影响遥感影像的解译与处理.本研究基于常用的地形辐射统计校正模型,结合山区植被覆盖特征,设计了一种分区统计校正模型,并采用三种不同分辨率的卫星遥感影像进行了实验,结果显示改进后模型的地形辐射校正效果有明显改善.     

山区遥感影像地形辐射统计校正模型改进研究

山区地形起伏导致太阳光照不同,造成遥感影像接收的辐射信息不能反映地表的真实信息,从而影响遥感影像的解译与处理.本研究基于常用的地形辐射统计校正模型,结合山区植被覆盖特征,设计了一种分区统计校正模型,并采用三种不同分辨率的卫星遥感影像进行了实验,结果显示改进后模型的地形辐射校正效果有明显改善.     

浅析遥感图像的几何校正原理及方法

几何校正,就是清除遥感图像中的几何变形,是遥感影像应用的一项重要的前期处理工作。本文简单分析了几何校正的原理和基本方法,并以ERDAS软件为例,对青海海东地区遥感影像进行了几何校正,从而直观地表述了遥感图像几何校正的完整过程。结果表明,几何校正的精度受多方面因素影响,最主要的是控制点GCP的选取数量和选取位置。本次校正精度小于0.5个像元,符合要求。     

光学遥感观测技术在IDI保险行业的应用

IDI保险是工程质量类保险的一种,在国内主要应用于住宅项目。IDI保险需要在工程建设的全过程中进行质量管控和风险评估。常规水准检测、GPS等技术难以满足IDI保险对风险建筑进行大范围、快速、长期监测的需要。光学遥感观测技术是一种可以实现大面积地物监测的遥感观测技术,该技术可以为IDI保险的监测工作提供数据信息支持。论文整理了近几年关于光学遥感观测技术应用于建筑高度监测的文献,对分类法、边缘检测法、阈值法进行介绍和总结,说明光学遥感观测技术在IDI保险行业中有较高的应用价值和广阔的应用前景。     

遥感影像辅助地图制图研究

随着社会经济的快速发展,人们的生活品质逐渐提高,对地图系统的要求也越来越高。文章主要阐述了传统制图存在的不足之处,并介绍了卫星影像应用在地理信息挖掘中的优势及制图方法,以此来加深对遥感影像辅助地图制图的了解。     

基于GIS的遥感影像分类方法研究

在分析空间数据获取现状的基础上提出空间数据挖掘的必要性,对遥感影像分类技术和方法进行了研究,提出GIS平台和数据挖掘算法集成所挖掘的知识是其影像分类的重要知识源。最后通过实验对以上的研究和分析进行了验证。     

基于OpenCL的遥感影像算法设计研究

介绍以OpenCL为基础,探讨以GPU对遥感影像进行处理的方法。首先介绍利用OpenCL进行GPU运算的一般性概念,对比GPU运算给遥感影像处理算法带来的影响;其次以实际的遥感影像为例,探讨在使用GPU处理遥感影像时,如何应对前一步所述的影响;接着根据实验结果给出在遥感影像算法中使用OpenCL利用GPU进行运算的总结性经验。最后,对比分别采用GPU和CPU的算法,总结出GPU算法在遥感影像处理方面的方法。     

基于山区地形的小型无人机巡检车辆管理体系研究与探索

为提高电网巡检效率,降低线路巡检难度和风险,采用小型无人机和人工协同巡检作业模式是非常先进和高效的。但是,一般小型无人机的线路巡检工作往往需要在偏远的山区进行,而山区道路多半崎岖难行,这对无人机的安全、平稳运输提出了挑战。一旦无人机在运输过程中出现机体或部件的损伤,将会严重影响线路巡检工作的开展,同时造成巨大的经济损失。因此,基于山区地形的小型无人机巡检车辆的管理体系建设是十分重要的。     

城市物流系统模式的统计划分方法

本文首先论证了构建中心城市物流系统的必要性；其次以中心城市流通客体的流向、流量为主要参数，综合考虑影响中心城市物流系统的经济因素、产业结构和物流基础设施水平等，得出中心城市物流系统的三种模型，即产出型、消费型和综合型物流系统。     

Statistical Implementations of Agent-Based Demographic Models

A variety of demographic statistical models exist for studying population dynamics when individuals can be tracked over time. In cases where data are missing due to imperfect detection of individuals, the associated measurement error can be accommodated under certain study designs (e.g. those that involve multiple surveys or replication). However, the interaction of the measurement error and the underlying dynamic process can complicate the implementation of statistical agent-based models (ABMs) for population demography. In a Bayesian setting, traditional computational algorithms for fitting hierarchical demographic models can be prohibitively cumbersome to construct. Thus, we discuss a variety of approaches for fitting statistical ABMs to data and demonstrate how to use multi-stage recursive Bayesian computing and statistical emulators to fit models in such a way that alleviates the need to have analytical knowledge of the ABM likelihood. Using two examples, a demographic model for survival and a compartment model for COVID-19, we illustrate statistical procedures for implementing ABMs. The approaches we describe are intuitive and accessible for practitioners and can be parallelised easily for additional computational efficiency.     

Statistical Assessment of Numerical Models*

Evaluation of physically based computer models for air quality applications is crucial to assist in control strategy selection. The high risk of getting the wrong control strategy has costly economic and social consequences. The objective comparison of modeled concentrations with observed field data is one approach to assessment of model performance. For dry deposition fluxes and concentrations of air pollutants there is a very limited supply of evaluation data sets. We develop a formal method for evaluation of the performance of numerical models, which can be implemented even when the field measurements are very sparse. This approach is applied to a current U.S. Environmental Protection Agency air quality model. In other cases, exemplified by an ozone study from the California Central Valley, the observed field is relatively data rich, and more or less standard geostatistical tools can be used to compare model to data. Yet another situation is when the cost of model runs is prohibitive, and a statistical approach to approximating the model output is needed. We describe two ways of obtaining such approximations. A common technical issue in the assessment of environmental numerical models is the need for tools to estimate nonstationary spatial covariance structures. We describe in detail two such approaches.     

Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.