Discriminant Models of Uncertainty in Nominal Fields

Despite developments in error modeling in discrete objects and continuous fields, there exist substantial and largely unsolved conceptual problems in the domain of nominal fields. This article explores a novel strategy for uncertainty characterization in spatial categorical information. The proposed strategy is based on discriminant space, which is defined with essential properties or driving processes underlying spatial class occurrences, leading to discriminant models of uncertainty in area classes. This strategy reinforces consistency in categorical mapping by imposing class-specific mean structures that can be regressed against discriminant variables, and facilitates scale-dependent error modeling that can effectively emulate the variation found between observers in terms of classes, boundary positions, numbers of polygons, and boundary network topology. Based on simulated data, comparisons with stochastic simulation based on indicator kriging confirmed the replicability of the discriminant models, which work by determining the mean area classes based on discriminant variables and projecting spatially correlated residuals in discriminant space to uncertainty in area classes.     

类别制图及其误差建模的概念框架(英文稿)

尽管离散目标和连续场的误差建模已得到了发展, 名义场却存在实质性的和多半悬而未决的概念问题。致力于为确定信息和不确定特性整合出一个概念框架。这个概念模型是基于判别空间而构建的; 后者是由面状类别时空表象的特质或驱动过程定义的。这个模型通过加入特定类的平均结构( 其可进行基于判别变量的回归分析) 的方式, 奠定类别制图一致性的基础, 并且使基于尺度的误差建模变得更为简便易行。这种误差建模可以有效地仿效观测者在类别、边界位置、多边形个数和边界网络拓扑特性等方面的差异。通过基于模拟数据的实验, 与基于指示克里格的随机仿真结果相对比, 肯定判别空间模型在确定平均面状类别( 反映判别变量的平均响应) 以及空间不确定性( 实为空间自相关的残差在地理空间的镜像) 的复现性或可重复性。     

Singly- and Doubly-Constrained Methods of Areal Interpolation for Vector-based GIS

Traditionally, areal interpolation has referred to techniques for transferring attribute values from one partitioning of space to a different partition of space but this is only one of several situations that create the need for estimating unknown data values for areal units. This paper presents a categorization of four areal interpolation problems that includes the "missing" data problem, the traditional "alternative geography" problem, the overlay of a choropelth and an area-class data layer, and the overlay of two choropleth data layers and demonstrates the relationship between the last three problems and general spatial interaction modelling. The "alternative geography" and overlay of choropleth and area-class data layers mirrors a singly constrained spatial interaction model while the overlay of two choropleth layers is analogous to a doubly constrained interaction model. Iterative proportional fitting techniques with and without ancillary data are developed to solve these three classes of problems.     

Gradation as a Communication Device in Area-Class Maps

This paper proposes a methodology to assess gradation as a cartographic tool for communicating information in area-class maps. The communication model is used as a theoretical foundation, suggesting distinction between errors that occur in encoding and decoding of geographic information. The proposed methodology begins with the determination of a target level of encoding error. Map alternatives are constrained to achieve this target, with gradation considered as one variable in the map production process. The result is a series of maps of equal encoding accuracy but varying in the degree of gradation represented. The individual maps of the series can then be evaluated in terms of decoding accuracy. The methodology is demonstrated by producing a series of alternative forest region maps of New York, Pennsylvania, and New Jersey based on U.S. Forest Service data on tree genus distributions. The series ranges from a 4-class graded area-class map to a 13-class crisp map. The results show gradation to be a viable alternative to the proliferation of map classes as a means of cartographic communication.     

土地覆盖变化信息自适应抽样策略及其精度评估

提出了一种面向土地覆盖变化信息局域精度评估的自适应型抽样策略。结合待研究地图的土地覆盖变化信息局部特征（如土地覆盖变化类别、斑块大小、异质性和优势度）,探讨与土地覆盖变化信息精度显著相关的协变量,以预测精度的标准误差作为判断标准,识别需要提高精度预测结果可靠性的区域,以自适应地和逐步定位的方式进行样本采集。基于武汉地区的精度评价结果,自适应的抽取增加100个训练样本使得预测精度的确定系数提高了50.66%,而简单随机抽取的增加样本使得预测精度的确定系数提高了17.22%。试验表明,自适应型抽样策略能显著提高土地覆盖变化信息局域精度预测的抽样效益,减少预测精度的不确定性。模型选择的结果表明,土地覆盖变化类别和优势度指数是最优的协变量组合。     

Estimating the Effects of Map Error on Habitat Delineation for the California Spotted Owl in Southern California

The reliability of habitat maps that have been generated using Geographic Information Systems (GIS) and image processing of remotely sensed data can be overestimated. Habitat suitability and spatially explicit population viability models are often based on these products without explicit knowledge of the effects of these mapping errors on model results. While research has considered errors in population modeling assumptions, there is no standardized method for measuring the effects of inaccuracies resulting from errors in landscape classification. Using landscape‐scale maps of existing vegetation developed for the USDA Forest Service in southern California from Landsat Thematic Mapper satellite data and GIS modeling, we performed a sensitivity analysis to estimate how mapping errors in vegetation type, forest canopy cover, and tree crown size might affect delineation of suitable habitat for the California spotted owl (Strix occidentalis occidentalis). The resulting simulated uncertainty maps showed an increase in the estimated area of suitable habitat types. Further analysis measuring the fragmentation of the additional patches showed that they were too small to be useful as habitat areas.     

Change Estimates by Map Comparison: A Method to Reduce Erroneous Changes Due to Positional Error

A common method to assess land use/cover change (LUCC) is the comparison of digital maps of an area within a geographic information system (GIS). However, positional errors of the maps involved in the comparison affect this assessment and much of the change shown by means of this comparison may be an artifact due to these errors. This note presents a simple method to improve change estimates by detecting and correcting erroneous changes resulting from positional errors. It allows an important reduction of error in change area estimates and is likely to be useful in LUCC assessment studies.     

对空间数据不确定性研究的思考

对GIS中空间数据不确定性研究中存在的问题进行思考后提出了5点建议。主要包括:1)要区分GIS中确定性目标和不确定性目标;2)要区分用离散点逼近曲线/曲面的逼近误差和离散点自身量测误差及其传播;3)要研究GIS中的几何不确定性,更要重视研究属性和时态不确定性;4)要研究空间数据质量,更要研究空间信息服务的质量;5)空间数据不确定性研究要努力向实际应用转化。     

Statistical analysis of two 3-D registration and modeling strategies

The paper deals with the registration and modeling of multiple 3-D profile maps acquired from different viewpoints by light striping. We analyze the propagation of measurement and calibration errors to the registration parameters and further to the reconstructed model of the scene consisting of planar patches. The analysis is performed for two strategies. In the first strategy, the maps are registered simultaneously using the Levenberg–Marquardt method to update the registration parameters and the model computed afterwards while in the second one, the maps are registered sequentially against the model reconstructed up till then using the method of unit quaternions to update the registration parameters. Our statistical analysis thus combines the registration and modeling steps, and in registration, we determine the corresponding points either on the parametric domains of the maps or as closest points in 3-D using the information from the parametric domain to restrict the search for the closest points. We also point out the precise estimation of the covariance matrix of the solution given by the Levenberg–Marquardt method. We illustrate the results of our analysis with real data from a scale model of an urban area.     

Quantifying uncertainty in remote sensing-based urban land-use mapping

Land-use/land-cover information constitutes an important component in the calibration of many urban growth models. Typically, the model building involves a process of historic calibration based on time series of land-use maps. Medium-resolution satellite imagery is an interesting source for obtaining data on land-use change, yet inferring information on the use of urbanised spaces from these images is a challenging task that is subject to different types of uncertainty. Quantifying and reducing the uncertainties in land-use mapping and land-use change model parameter assessment are therefore crucial to improve the reliability of urban growth models relying on these data. In this paper, a remote sensing-based land-use mapping approach is adopted, consisting of two stages: (i) estimating impervious surface cover at sub-pixel level through linear regression unmixing and (ii) inferring urban land use from urban form using metrics describing the spatial structure of the built-up area, together with address data. The focus lies on quantifying the uncertainty involved in this approach. Both stages of the land-use mapping process are subjected to Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The robustness to uncertainty of the land-use mapping strategy is addressed by comparing the most likely land-use maps obtained from the simulation with the original land-use map, obtained without taking uncertainty into account. The approach was applied on the Brussels-Capital Region and the central part of the Flanders region (Belgium), covering the city of Antwerp, using a time series of SPOT data for 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original land-use map – indicating absence of bias in the mapping process – it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, uncertainties observed in the derived land-use maps should be taken into account when using these maps as an input for modelling of urban growth.     

联合稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类

零样本影像分类技术旨在通过学习数据集的部分类别（可见类）,获得识别在训练阶段未出现类别（不可见类）的能力。该技术在遥感大数据时代具有重要现实意义。目前,遥感领域的零样本场景分类方法对于映射后的语义空间优化关注很少,导致已有方法的整体分类性能较差。基于这一考虑,本文提出了一种基于稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类方法。在训练的有监督学习模块,基于可见类的类别语义向量和场景影像样本,实现深度特征提取器学习和视觉空间到语义空间的稳健映射。在训练的无监督学习阶段,基于全体类别的类别语义向量和不可见类遥感影像样本,分别通过协同表示学习和k近邻算法来渐进修正不可见类类别的语义向量,从而缓解可见类语义空间与不可见类语义空间的漂移问题和自编码跨域映射模型映射后不可见类语义空间与协同表示后不可见类语义空间的偏移问题。在测试阶段,基于学习所得的深度特征提取器、自编码跨域映射模型和修正后的不见类语义向量,实现对不可见类遥感影像场景的分类。本文整合多个已有公开的遥感影像场景数据集,组建了一个新的遥感影像场景数据集,在此数据集上进行试验。试验结果表明本文提出的算法在多种不同的可见类与不可见类的划分情况下都明显优于已有公开零样本分类方法。     

基于时空尺度的线位统一不确定性模型

在GIS矢量数据中,线对象在建模过程中存在的建模误差是必然的,并且成为不确定性内容中的重要组成部分,而现有的线位不确定性模型则往往将其忽略,本文充分考虑建模误差,采用误差传播律,对附有建模误差的二维折线不确定性模型进行估计,其中,e带模型、g带模型是其中的一个特例,即当且仅当建模误差为零时,便是e带模型、g带模型.     

Comparison of upscaling cropland and non-cropland map using uncertainty weighted majority rule-based and the majority rule-based aggregation methods

Aggregation method is seriously impacted by the landscape characteristics, which has been emphasized due to proportional errors. This research proposed an uncertainty weighted majority rule-based aggregation method (UWMRB) to upscale the cropland/non-cropland map. The Cropland Data Layer for 2016 at 30m resolution, with its corresponding confidence level data, were collected to conduct the experiment using UWMRB and majority rule-based aggregation method. Proportional errors of crop/non-crop were used to assess the accuracy of the two methods. Ordinal logistic regression was used to obtain the probability of an error occurring to predict the uncertainty of both methods. The results show that UWMRB can achieve the lower proportional errors with lower uncertainty. Also, it can reduce the influence of complexity and fragmentation of landscape on aggregation performance. Additionally, the examination of UWMRB provides an important view of application of uncertainty information for upscaling land cover maps in an efficient way.     

Revealing Uncertainties in Land Change Modeling Using Probabilities

Land change models are frequently used to analyze current land change processes and possible future developments. However, the outcome of such models is accompanied by uncertainties that have to be taken into account in order to address their reliability for science and decision‐making. While a range of approaches exist that quantify the disagreement of land change maps, the quantification of uncertainty remains a major challenge. The aim of this article is therefore to reveal uncertainties in land change modeling by developing two measures: quantity uncertainty and allocation uncertainty. We choose a Bayesian Belief Network modeling approach for deforestation in Brazil to develop and apply the two measures to the resulting probability surface. Quantity uncertainty describes the uncertainty about the correct number of cells in a land change map assigned to different land change categories and allocation uncertainty expresses the uncertainty about the correct spatial placement of a cell in the land change map. Thus, uncertainty can be quantified even in those cases where no reference data exist. Informing about uncertainty in probabilistic outcomes may be an important asset when land change projections are being used in science and decision‐making and moreover, they may also be further evaluated for other spatial applications.     

Accounting for image uncertainty in SAR-based flood mapping

Operational flood mitigation and flood modeling activities benefit from a rapid and automated flood mapping procedure. A valuable information source for such a flood mapping procedure can be remote sensing synthetic aperture radar (SAR) data. In order to be reliable, an objective characterization of the uncertainty associated with the flood maps is required.This work focuses on speckle uncertainty associated with the SAR data and introduces the use of a non-parametric bootstrap method to take into account this uncertainty on the resulting flood maps. From several synthetic images, constructed through bootstrapping the original image, flood maps are delineated. The accuracy of these flood maps is also evaluated w.r.t. an independent validation data set, obtaining, in the two test cases analyzed in this paper, F-values (i.e. values of the Jaccard coefficient) comprised between 0.50 and 0.65. This method is further compared to an image segmentation method for speckle analysis, with which similar results are obtained. The uncertainty analysis of the ensemble of bootstrapped synthetic images was found to be representative of image speckle, with the advantage that no segmentation and speckle estimations are required.Furthermore, this work assesses to what extent the bootstrap ensemble size can be reduced while remaining representative of the original ensemble, as operational applications would clearly benefit from such reduced ensemble sizes.     

Knowledge Discovery from Area-Class Resource Maps: Data Preprocessing for Noise Reduction

Spatial data mining techniques have been investigated to extract valuable knowledge from natural resource maps. One challenge in applying general‐purpose data mining algorithms to knowledge discovery from these maps is that the unavoidable existence of errors in traditional area‐class resource maps introduces noise to the process and impacts the accuracy of the extracted knowledge. This paper examines the effect of data preprocessing in knowledge discovery from area‐class resource maps. Presented here is a sampling method designed to obtain samples that exclude noise and are representative of the central concepts of the mapped classes. The method is based on selecting only the pixels that fall in modes of environmental histograms for each individual class. A case study in extracting knowledge from soil maps shows that data preprocessing plays an important role in the knowledge discovery process, and that the sampling method based on environmental histograms significantly improves the knowledge discovery performance.     

Linking socioeconomic classes and land cover data in Lima,Peru: Assessment through the application of remote sensing and GIS

The spatial differentiation of socioeconomic classes in a city can deliver insight into the nexus of urban development and the environment. The purpose of this paper is to identify poor and rich regions in large cities according to the predominant physical characteristics of the regions. Meaningful spatial information from urban systems can be derived using remote sensing and GIS tools, especially in large difficult-to-manage cities where the dynamics of development results in rapid changes to urban patterns. We use here very high resolution imagery data for the identification of homogeneous socioeconomic zones in a city. We formulate the categorization task as a GIS analysis of an image classified with conventional techniques. Experiments are conducted using a QuickBird image of a study area in Lima, Peru. We provide accuracy assessment of results compared to ground truth data. Results show an approximated allocation of socioeconomic zones within Lima. The methodology described could also be applied to other urban centers, particularly large cities of Latin America, which have characteristics similar to those of the study area.     

遥感信息不确定性研究

近年来遥感技术及遥感信息产业化发展迅猛 ,但遥感信息的不确定性制约着遥感信息的产品化和实用化的进一步发展。虽然 ,这一问题得到了国内外众多学者的关注 ,并提出和采用相关理论和方法进行分析 ,取得了相当的进展 ,但这些方法在分析遥感信息不确定性时忽略了一个重要的研究点 :遥感信息的不确定性传递机理。本文主要目标就是建立一套遥感信息不确定性的处理方法     

局部表面参数化的实景三角网模型语义增强方法

倾斜摄影测量方法已可自动获取城市规模的实景三角网模型,然而散乱的三角网缺乏精细的几何结构和功能语义信息。为克服上述问题,提出一种局部表面参数化的实景三角网模型语义增强方法:将具有语义信息的独立三维部件与实景三角网模型的无缝融合问题,通过定义三维表面结构树,转换为局部区域的三角网替换操作;在待融合区域附近,将原实景三角网模型和替换的三维语义部件,通过局部参数化表达,UV展开为二维平面三角网;在二维平面上构建约束Delaunay三角网(CDT),实现两模型的无缝拼接,逆映射至三维空间并自动重建语义部件。通过深圳某区域的倾斜影像进行的试验证明,本文方法能有效实现具有开放边界和语义信息的部件模型与表面模型的无缝融合。与商业软件Maya对比,这种基于插入、融合的手段对提高建模效率具有实用价值。