Shape detection of multiple subsurface cavities by particle filtering with elastic wave propagation

A numerical technique for detecting the number and shape of subsurface cavities is presented, applying the particle filter and the parametric level set method to elastic wave propagation under the ground. When subsurface cavities exist, the elastic wave propagating in the ground is reflected at the boundary faces of the cavities. Observing the velocity of the reflection wave at the surface of a ground that includes multiple cavities and parameterizing the shape of the cavities by the parametric level function, both the number and the shape of the cavities can be identified by the particle filter. Numerical experimentation for detecting multiple cavities is conducted with synthetic observation data. The results show that the proposed technique enables the number of cavities to be identified by the number of peaks in the posterior probabilistic distribution function and solves geometric inverse problems by estimating the shape of the cavities through the parameter identification of the level set function.     

Transferability of hydrological model parameter spaces in the estimation of runoff in ungauged catchments

Abstract

In this study, transferability options of the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model parameter (MP) spaces were investigated to estimate ungauged catchment runoff. Three approaches were applied in the study: MP space transfer from single, neighbouring and all potential donor catchments. The model performance was evaluated by a jackknife procedure, where one catchment at a time was treated as if ungauged, and behavioural MP sets from candidate donor catchments were used to estimate the “ungauged” runoff. The results showed that ungauged catchment runoff estimation could not be guaranteed by transferring MP sets from a single physiographically nearest donor catchment. Integrating MP sets typically from one to six donor catchments supplemented the lack of effective MP sets and improved the model performance at the ungauged catchments. In addition, the analysis results revealed that the model performance converged to an average performance when the MP sets of all potential donor catchments were integrated.     

面向对象的高分辨率遥感影像信息提取技术

介绍了单张高空间分辨率的航空遥感数据在没有其他专题图辅助的情况下进行面向对象的信息提取的方法。以山西省平顺县为研究区,ADS40彩色航空影像为数据源,对不同的地物采取不同尺度的多尺度分割,形成具有"同质性"的对象,然后分别采取不同的信息提取策略,得到研究区信息分类成果,且有效避免了"同谱异物"、"同物异谱"及"椒盐现象",对高分辨率遥感影像信息提取技术研究具有一定的实践指导意义。     

Impact of discretization methods on the rough set-based classification of remotely sensed images

Abstract

In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.     

Geo-rough space

Rough set is a new approach to uncertainties in spatial analysis. In this paper, rough set symbols are simplified and standardized in terms of rough interpretation and specialized indication. Rough spatial entities and their topological relationships are also proposed in rough space, thus a universal intersected equation is developed, and rough membership function is further extended with the gray scale in our case study. We complete three works. First, a set of simplified rough symbols is advanced on the basis of existing rough symbols. Second, rough spatial entity is put forward to study the real world as it is, without forcing uncertainties into crisp set. Third, rough spatial topological relationships are studied by using rough matrix and their figures. The relationships are divided into three types, crisp entity and crisp entity (CC), rough entity and crisp entity (RC), and rough entity and rough entity (RR). A universal intersected equation is further proposed. Finally, the maximum and minimum maps of river thematic classification are generated via rough membership function and rough relationships in our case study.     

Aggregation in continuous space coverage modeling

Uncertainties and errors associated with aggregation have long been recognized in the study of spatial problems. In facility location modeling, while much has been done to examine the aggregation of large datasets of discrete points, errors and uncertainties involved in aggregating continuous spatial units are not well understood. This study focuses on the effects of aggregating continuous spatial units into discrete points within the context of the location set covering problem. We propose new measures to understand and quantify errors associated with a continuous aggregation scheme. In a real-world application, the proposed methods can be used to suggest an appropriate aggregation scheme before the application of the location model. We demonstrate the concepts developed here with an empirical study of siting emergency warning sirens in the city of Dublin, OH.     

Using rough set theory to identify villages affected by birth defects: the example of Heshun,Shanxi, China

This article uses rough set theory to explore spatial decision rules in neural-tube birth defects and searches for novel spatial factors related to the disease. The whole rule induction process includes data transformation, searching for attribute reducts, rule generation, prediction or classification, and accuracy assessment. We use Heshun as an example, where neural-tube birth defects are prevalent, to validate the approach. About 50% of the villages in Heshun are used as the sample data, from which all of the rules are extracted. Meanwhile, the other villages are used as reference data. The rules extracted from the training data are then applied to the reference data. The result shows that the rules' generalization is reasonably good. Moreover, a novel relationship between the spatial attributes and the neural-tube birth defects was discovered. That is, the villages that lie in Watershed 9 of this district and that are also associated with a gradient of between 16° and 25° are vulnerable to neural-tube birth defects. This result paves the road for predicting where high rates of neural-tube birth defects will occur and can be used as a preliminary step in finding a direct cause for the disease.     

基于SPA的中国沿海旅游城市经济系统脆弱性评价

运用集对分析法从经济敏感性和应对能力两方面对中国29个典型沿海旅游城市经济系统脆弱性进行测度.结果表明:1)沿海旅游城市经济系统脆弱性呈现出一定的区域差异:绍兴、南通、唐山、宁波为低脆弱性区域,而广州、秦皇岛、三亚、深圳、海口为高脆弱性区域;2)经济敏感性和应对能力共同决定沿海旅游城市经济系统脆弱性程度;3)根据障碍因素分析,总游客量增长率、旅游外汇收入占旅游总收入比重、旅游业增长弹性系数、旅游从业人员占总从业人员比重等敏感性因子及实际利用外资占全市GDP比重、教育支出占地方财政支出比重、人均GDP、产业结构多样化指数等应对能力因子是制约系统脆弱性降低的关键障碍因素.     

粗糙集理论与方法在地质研究中的应用

在地质研究工作中,经常遇到海量的数据需要处理.这些数据集合通常具有规模庞大、类型复杂、参数繁多、完整性差、先验信息多等特点.因此,地质数据集合分析一直是制约地质工作定量化研究的瓶颈.本文针对地质研究中数据集合的特点,结合粗糙集理论,提出一种适合地质研究领域泛用的地质数据分析和规则提取模型,并通过三个实例介绍应用该模型约...     

Building roof modeling from airborne laser scanning data based on level set approach

This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log₂n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.