Wenzhong Shi

Spatial data quality

Measurement GIS and Geostatistics. Model Spatial Objects with Uncertainty. Spatial Data Quality Control .Quality Management. Communicating Uncertainty and Resolution. Reasoning and Decision Making. Visualization of Uncertainty. Error Metadata.

Quality assessment for geo‐spatial objects derived from remotely sensed data

Airborne laser scanners and multi‐spectral scanners provide information on height and spectra that offer exciting possibilities for extracting features in complicated urban areas. We apply an object‐based approach to building extraction from image data in an approach that differs from conventional per‐pixel approaches. Since image objects are extracted based on the thematic and geometric components of objects, quality assessments will have to be made object‐based with respect to these components. The known per‐pixel‐based methods for assessing quality have been examined in the new situation as well as their limitations. A new framework for carrying out quality assessments by measuring the similarity between the results of feature extraction and reference data is proposed in this paper. The proposed framework consists of both per‐object and per‐pixel measures of quality, thus providing measures pertaining to qualitative and quantitative measurements of object quality from thematic and geometric aspects. The proposed framework and measures of quality have been applied to an assessment of the results of object‐based building extraction using high‐resolution laser data and multi‐spectral data in two test cases. The results show that the per‐object‐based method of assessing quality gives additional information to conventional per‐pixel, attribute‐only assessment methods.

A stochastic process-based model for the positional error of line segments in GIS

This paper presents a model for describing the positional error of line segments in geographical information systems (GIS). The model is based on stochastic process theory with the assumptions that the errors of the endpoints of a line segment follow two-dimensional normal distributions. The distribution and density functions of the line segments are derived statistically. The uncertainty information matrix of line segments is derived to indicate the error of an arbitrary point on the line segment. This model covers the cases where two-end points are correlated to each other and points on the line segment are stochastically continuous to each other. The model is a more generic error band model than those previously developed and is called the G-Band model.

Development of Voronoi-based cellular automata -an integrated dynamic model for Geographical Information Systems

This paper presents a development of the extended Cellular Automata (CA), a Voronoi-based CA, to model dynamic interactions among spatial objects. Cellular automata are efficient models for representing dynamic spatial interactions. A complex global spatial pattern is generated by a set of simple local transition rules. However, its original definition for a two-dimensional array limits its application to raster spatial data only. This paper presents a newly developed Voronoi-based CA in which the CA is extended by using the Voronoi spatial model as its spatial framework. The Voronoi spatial model offers a ready solution to handling neighbourhood relations among spatial objects dynamically. By implementing this model, we have demonstrated that the Voronoi-based CA can model local interactions among spatial objects to generate complex global patterns. The Voronoi-based CA can further model interactions among point, line and polygon objects with irregular shapes and sizes in a dynamic system. Each of these objects possesses its own set of attributes, transition rules and neighbourhood relationships. The Voronoi-based CA models spatial interactions among real entities, such as shops, residential areas, industries and cities. Compared to the original CA, the Voronoi-based CA is a more natural and efficient representation of human knowledge over space.

A generic statistical approach for modelling error of geometric features in GIS

This paper describes a newly developed statistical approach for modelling positional error of geometric features in GIS. The generic statistical models for N-dimensional features are firstly derived. The models for one- and twodimensional features are then developed as the specific cases of the generic models. In each dimension, the GIS features are classified as points, line segments and line features. Because of the errors, features stored in GIS may not correspond with their actual location in the real world. The true location of a GIS feature is only known within a certain area around the represented location in GIS. This newly developed approach can be used to provide a statistical description of such areas. For one-, two- and N-dimensional GIS features, they are defined as confidence intervals, confidence regions and confidence spaces respectively. The areas are related to the positional errors of the composite points of the features and to the predefined confidence level. The models are derived bas...

Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM

The panchromatic (Pan) sharpening of multispectral (MS) bands is an important technique in the various applications of satellite remote sensing. This paper presents an MS Pan- sharpening method using the proposed multiscale mapped least-squares support vector machine (LS-SVM). Under the LS-SVM framework, the salient features underlying the image are represented by support values, and the support value transform (SVT) is developed for image information extraction. The low-resolution MS bands are resampled to the fine scale of the Pan image and sharpened by injecting the detailed features extracted from the high-resolution Pan image. The support value analysis is implemented by using a series of multiscale support value filters that are deduced from the mapped LS-SVM with multiscale Gaussian radial basis function kernels. Experiments are carried out on very high resolution QuickBird MS + Pan data. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed MS Pan-sharpening method performs comparable to the state-of-the-art in terms of the pertained quantitative quality evaluation indexes, such as the Spectral Angle Mapper, relative dimensionless global error in synthesis (ERGAS), modulation-transfer-function-based tool and quality index (Q4), etc. The SVT is an effective tool for remote sensing image fusion.

An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery

Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Gearys C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.

Thirty Years of Research on Spatial Data Quality: Achievements, Failures, and Opportunities

This article reflects on the past 30 years of academic research in the field of spatial data quality and tries to identify the main achievements, failures, and opportunities for future research. Most of this reflection results from a panel discussion that took place during the Sixth International Symposium on Spatial Data Quality (ISSDQ) in July 2009.

Performance Evaluation of Line Simplification Algorithms for Vector Generalization

Abstract Many studies of line simplification methods have been developed; however, an evaluation of these methods is still an open issue. This paper aims to evaluate a diversity of automatic line simplification algorithms in terms of positional accuracy and processing time. Past research studies for the performance evaluation were centred on measuring the location difference between a line to be simplified and its simplified version. However, the original line contains positional uncertainty. This paper evaluates performance of the line simplification algorithms using two comprehensive measures of positional accuracy of the simplified line. These two measures include one displacement measure and one shape distortion measure, both of which are able to consider (a) the displacement between the original line and its simplified version, and (b) positional uncertainty of the original line.

The line segment match method for extracting road network from high-resolution satellite images

This paper presents an approach, with emphasis on the newly proposed line segment matching method, for extracting urban road networks from high-resolution satellite images. The approach is based on the characteristics of the images, knowledge about road networks, and the related mathematical models. The approach is applied to several images of urban areas and is proved to be effective in both visual effect and positional accuracy.