Automatic detection of a one dimensional ranging pole for robust external camera calibration in mobile mapping

A mobile mapping system (MMS) is the answer of the geoinformation community to the exponentially growing demand for various geospatial data with increasingly higher accuracies, captured by multiple sensors. As the mobile mapping technology is pushed to explore its use for various applications on water, rail, or road, the need emerges to have an external sensor calibration procedure that is portable, fast and easy to perform. This way, sensors can be mounted and demounted depending on the application requirements without the need for time consuming calibration procedures. A new methodology is presented to provide a high quality external calibration of cameras which is automatic, robust and fool proof. The method uses a portable, standard ranging pole which needs to be positioned on a known ground control point. While the literature focuses on solving the absolute orientation problem of the calibration, an automatic method to detect the calibration object is missing. Here, we present a mutual information based image registration technique for automatic sub-pixel localization of the ranging pole under realistic outdoor conditions. We include several robust error detection rules to allow the calibration to run without human intervention, giving as little outliers as possible, to ensure a correct calibration. Several tests are performed under various lighting and noise conditions which prove the methodology’s robustness.     

Spatial Differences in Multi-Resolution Urban Automata Modeling

The last decade has seen a renaissance in spatial modeling. Increased computational power and the greater availability of spatial data have aided in the creation of new modeling techniques for studying and predicting the growth of cities and urban areas. Cellular automata is one modeling technique that has become widely used and cited in the literature; yet there are still some very basic questions that need to be answered with regards to the use of these models, specifically relating to the spatial resolution during calibration and how it can impact model forecasts. Using the SLEUTH urban growth model ( Clarke et al. 1997 ), urban growth for San Joaquin County (CA) is projected using three different spatial grains, based on four calibration routines, and the spatial differences between the model outputs are examined. Model outputs show that calibration at finer scaled data results in different parameter sets, and forecasting of urban growth in areas that was not captured through the use of more coarse data.     

Automated Parameterization of Land Surface Process Models Using Fuzzy Logic

All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non–specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub–canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could     

Integrating a Raster Geographical Information System with Multi‐Objective Land Allocation Optimization for Conservation Reserve Design

The multi‐objective land allocation problem is to optimize the selection of land for different uses based on a set of decision objectives. For most applications, a geographical information system (GIS) is either absent or loosely coupled through file exchange. In this article the evolutionary algorithm (EA), a heuristic solution method for optimization problems, is integrated with a raster GIS to form a spatial decision support system (SDSS) for multi‐objective conservation reserve design. The SDSS effectively combines the functions of a GIS for data management, analysis, and visualization, with the optimization capability of the EA; and provides a uniform way to solve conservation reserve design problems with different types of constraints and objectives. The SDSS is demonstrated through application to the creation of conservation reserves in Bolivia to protect 17 endemic mammals.     

Temporal Accuracy in Urban Growth Forecasting: A Study Using the SLEUTH Model

This study attempts to establish multi‐temporal accuracy of the predicted maps produced by a land use change simulation model over time. Validation of the forecasted results is an essential part of predictive modeling and it becomes even more important when the models are used for decision making purposes. The present study uses a popular land use change model called SLEUTH to investigate the temporal trend of accuracy of the predicted maps. The study first investigates the trend of accuracy of the predicted maps from the immediate future to the distant future. Secondly, it investigates the impact of the prediction date range on the accuracy of the predicted maps. The objectives are tested for the city of Gorizia (Italy) using three sets of map comparison techniques, Kappa coefficients, Kappa Simulation and quantity disagreement and allocation disagreement. Results show that, in addition to the model's performance, the decrease in the accuracy of the predicted maps is dependent on factors such as urban history, uncertainty of input data and accuracy of reference maps.     

航空倾斜摄影系统多相机姿态安装误差检校

针对航空倾斜摄影系统多相机间相对关系的姿态误差,提出一种利用下视影像密集匹配点云约束的联合平差多相机姿态安装误差检校方法。文中方法以共线方程为基础,通过联立倾斜立体像对连接点、下视影像密集匹配点云数据提供的高程约束虚拟观测值,采用非线性最小二乘SQPM算法求解倾斜影像的外方位元素改正数,从而解算下视相机与倾斜相机间的姿态安装误差。为了保证连接点可靠性,提高虚拟观测值的有效性,采用下视影像密集匹配的点云约束倾斜影像立体连接点匹配和分布优化过程。利用SWDC-5倾斜摄影系统获取的数据进行实验,通过原始姿态检校参数将下视影像生成的密集点云投影至倾斜立体影像上,在像方有22个像素左右的误差,采用文中方法检校后,像方误差减少到0.29个像素。     

船载三维激光扫描系统安置参数标定方法

船载三维激光扫描技术在海洋测绘中具有重要的理论意义和实用价值。安置参数标定作为船载三维激光扫描系统测量的重要步骤,是当前亟待解决的问题。本文提出了一种无地面控制点的船载三维激光扫描系统安置参数标定方法。该方法在船载三维激光扫描数据时空配准模型的基础上,根据扫描测线重叠区域内的同名特征点,推导扫描系统安置参数标定模型,最后采用差分最小二乘算法求解参数最优值。试验验证了本文方法的合理性和有效性,能够显著提高扫描数据的质量。     

Satellite selection with an end-to-end deep learning network

Benefiting from multi-constellation Global Navigation Satellite Systems (GNSS), more and more visible satellites can be used to improve user positioning performance. However, due to limited tracking receiver channels and power consumption, and other issues, it may be not possible, or desirable, to use all satellites in view for positioning. The optimal subset is generally selected from all possible satellite combinations to minimize either Geometric Dilution of Precision (GDOP) or weighted GDOP (WGDOP). However, this brute force approach is difficult to implement in real-time applications due to the time- and power-consuming calculation of the DOP values. As an alternative to a brute force satellite selection procedure, the authors propose an end-to-end deep learning network for satellite selection based on the PointNet and VoxelNet networks. The satellite selection is converted to a satellite segmentation problem, with specified input channel for each satellite and two class labels, one for selected satellites and the other for those not selected. The aim of the satellite segmentation is that a fixed number of satellites with the minimum GDOP/WGDOP value can be segmented from any feeding order of input satellites. To validate the proposed satellite segmentation network, training and test data from 220 IGS stations tracking GPS and GLONASS satellites were used. The segmentation performance using different architectures and representations of input channels, including receiver-to-satellite unit vector and elevation and azimuth, were compared. It was found that the input channel with elevation and azimuth can achieve better performance than using the receiver-to-satellite unit vector, and an architecture with stacked feature encoding (FE) layers has better satellite segmentation performance than one without stacked FE layers. In addition, the models with GDOP and WGDOP criteria for selecting 9 and 12 satellites were trained. It was demonstrated that the satellite segmentation network was about 90 times faster than using the brute force approach. Furthermore, all the trained models can effectively select the satellites making the most contribution to the desired GDOP/WGDOP value. Approximately 99% of the tests had GDOP and WGDOP value differences smaller than 0.03 and 0.2, respectively, between the predicted subset and the optimal subset.     

TM数据在土地利用动态监测中的应用

土地利用动态监测的目的在于定期或不定期地了解土地利用的动态变化趋势,为土地资源的规划和治理提供依据。本文介绍TM数据在土地利用动态监测中的应用方法,阐述了减少夸大变化程度的根本措施在于提高每次单独分类的精度及不同时相遥感数据的空间配准精度。通过对多时相TM数据单独分类结果的对比,得出土地利用动态变化信息     

A Service Oriented Geoprocessing System for Real‐Time Road Traffic Monitoring

The emergence of the Sensor Web has paved the way for a new set of innovative software applications that exploit the enhanced availability of real‐time information. This article describes one such application built from Sensor Web components that aggregates GPS track data from a fleet of vehicles to provide an overview of road traffic congestion at the city scale. The application embodies a Service Oriented Architecture; web service components are used to archive and pre‐process incoming sensor observations, to encapsulate a horizontally partitioned spatial database that performs geoprocessing, and to disseminate results to client applications. Our results confirm that floating car data can provide an accurate depiction of current road traffic conditions. The presented solution uses Open Geospatial Consortium web services where possible and serves to highlight the difficulties inherent in achieving horizontal database scalability in sensor based geoprocessing systems.     

参数有界约束下的最小二乘平差算法

文中基于测量不确定度理论,建立参数有界约束下的平差模型及其解算方法。顾及变形监测网特点,将该平差模型及算法应用到沉降监测网实例中,即利用已知先验信息,建立相应约束模型,求得有界条件下的参数最优估值。通过与经典最小二乘平差法比较,结果证明模型的有效性,估计的参数值控制在给定范围内且不"失真",其沉降量更接近实际情况。     

Monitoring GOCE gradiometer calibration parameters using accelerometer and star sensor data: methodology and first results

The Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite, launched on 17 March 2009, is designed to measure the Earth’s mean gravity field with unprecedented accuracy at spatial resolutions down to 100?km. The accurate calibration of the gravity gradiometer on-board GOCE is of utmost importance for achieving the mission goals. ESA’s baseline method for the calibration uses star sensor and accelerometer data of a dedicated calibration procedure, which is executed every 2?months. In this paper, we describe a method for monitoring the evolution of calibration parameter during that time. The method works with star sensor and accelerometer data and does not require gravity field models, which distinguishes it from other existing methods. We present time series of calibration parameters estimated from GOCE data from 1 November 2009 to 17 May 2010. The time series confirm drifts in the calibration parameters that are present in the results of other methods, including ESA’s baseline method. Although these drifts are very small, they degrade the gravity gradients, leading to the conclusion that the calibration parameters of the ESA’s baseline method need to be linearly interpolated. Further, we find a correction of ?36 × 10^?6 for one calibration parameter (in-line differential scale factor of the cross-track gradiometer arm), which improves the gravity gradient performance. The results are validated by investigating the trace of the calibrated gravity gradients and comparing calibrated gravity gradients with reference gradients computed along the GOCE orbit using the ITG-Grace-2010s gravity field model.     

COSMO-SkyMed颜色变换强度RC法南京建设用地变化检测

基于非局部滤波的SAR强度RC合成变化检测法对小图斑、线型地物等动态监测灵敏,且对数据获取无时空基线要求,在多云多雨城市地表要素变化检测中具备潜力。本文研究以多时相SAR强度RC合成图为数据源,提出一种基于色彩空间变换的变化图斑半自动提取方法,即通过色彩空间转换、训练样本选取、监督分类影像分割、变化区域提取4步骤,可实现基于SAR强度图的城市建设用地动态监测与图斑高效更新。选取南京河西新城与江北新区为示范,以最优参数配置（3特征向量与10样本类别）进行试验,实现了优于88%的建设用地查准率指标。     

A Bayesian estimator for linear calibration error effects in thermal remote sensing

The Bayesian land surface temperature estimator previously developed has been extended to include the effects of imperfectly known gain and offset calibration errors. It is possible to treat both gain and offset as nuisance parameters and, by integrating over an uninformative range for their magnitudes, eliminate the dependence of surface temperature and emissivity estimates upon the exact calibration error.     

Principal Point Behaviour and Calibration Parameter Models for Kodak DCS Cameras

Digital still cameras have been widely adopted for close range photogrammetry and machine vision applications. Due to the advantages of onboard storage of digital images, portability and rapid data processing, digital still cameras are rapidly becoming standard equipment for measurement tasks such as industrial metrology and heritage recording. As for any metric application, the accuracy of the derived object data is dependent, amongst many other factors, on the accuracy of the camera calibration. For the vast majority of photogrammetric applications, use of the simple case of a block invariant calibration model comprising the primary physical parameters, including the principal point position, is sufficient. However, cameras designed for photojournalism and domestic use, such as the Kodak DCS420 and 460 cameras, are well known for their calibration instability because the design is based on a 35 mm SLR camera body. In particular, previous research has shown that the principal point location is prone to movement during normal handling of the camera, due to the mounting mechanism of the CCD array. This paper reports on an investigation of the physical behaviour of the principal point location and compares different calibration parameter models for the Kodak DCS420 and DCS460 digital still cameras.     

中国陆地1km AVHRR数据集

介绍了中国陆地范围的长序列AVHRR数据集及处理方法。数据处理链包括辐射标定、导航定位、几何精纠正、云检测、大气纠正、双向反射纠正以及多时相数据合成等一系列过程。大气校正采用SMAC方法．利用每日的大气参数对臭氧、瑞利散射、气溶胶和水汽柱等4个主要大气因子的影响进行了纠正。利用地面能见度和水汽压信息反演气溶胶光学厚度，利用最大植被指数法合成旬数据集。完成了1991-2003年的AVHRR数据集处理，形成了标准的数据集。     

构造地理要素趋势面的尺度转换普适性方法探讨

当前的遥感科学面临着遥感数据获取能力与数据应用能力之间突出的供需矛盾。尺度问题作为遥感科学中的关键问题,既限制了遥感作为一门科学向系统性、普适性的发展,又限制了遥感应用能力的发展。本文对定量遥感中的尺度问题进行了梳理,包括:遥感与传统站点观测之间的不一致、不同尺度遥感产品之间的不一致、机理模型的尺度适用问题,以及遥感产品与用户需求时空尺度间的不一致。对遥感中的尺度转换方法展开了讨论,总结了尺度转换的关键问题在于原数据信息量不足时引入额外信息和保留关键信息两方面。提出了构造地理要素趋势面的基本构想,搭建了一个具有普适性的尺度转换方法框架。核心内容是充分利用地表环境要素时间、空间上的信息作为先验知识,通过关联遥感观测新信息和先验趋势面生成指定时空尺度的地表要素产品。     

遥感高时空融合方法的研究进展及应用现状

针对遥感图像的"时空矛盾",评述了当前解决这一问题最主要的方法即遥感时空信息融合的方法,包括基于变化模型的融合、基于重建模型的融合以及基于学习模型的融合。通过分析各个模型的研究现状,指出了每种模型方法的优劣,特别重点介绍了影响较大的自适应时空融合方法的理论以及对其的改进算法。同时本文总结了当前时空融合模型在长时间序列模拟以及大区域数据集生成等方面的实际应用的效果,以及分析了影响时空融合结果的主要因素。最后基于这些问题和影响因素提出了今后时空融合模型发展的目标和方向。     

A multi‐label cellular automata model for land change simulation

The use of cellular automata (CA) has for some time been considered among the most appropriate approaches for modeling land‐use changes. Each cell in a traditional CA model has a state that evolves according to transition rules, taking into consideration its own and its neighbors’ states and characteristics. Here, we present a multi‐label CA model in which a cell may simultaneously have more than one state. The model uses a multi‐label learning method—a multi‐label support vector machine, Rank‐SVM—to define the transition rules. The model was used with a multi‐label land‐use dataset for Luxembourg, built from vector‐based land‐use data using a method presented here. The proposed multi‐label CA model showed promising performance in terms of its ability to capture and model the details and complexities of changes in land‐use patterns. Applied to historical land use data, the proposed model estimated the land use change with an accuracy of 87.2% exact matching and 98.84% when including cells with a misclassification of a single label, which is comparably better than a classical multi‐class model that achieved 83.6%. The multi‐label cellular automata outperformed a model combining CA and artificial neural networks. All model goodness‐of‐fit comparisons were quantified using various performance metrics for predictive models.     

Airborne MSS for land cover classification II

A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per‐field sampling and the addition of low‐pass filtering, image texture, prior probabilities and two dates of imagery.

For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per‐field sampling accounted for the largest proportion of this increase.