Fangju Wang

Fuzzy supervised classification of remote sensing images

A fuzzy supervised classification method in which geographical information is represented as fuzzy sets is described. The algorithm consists of two major steps: the estimate of fuzzy parameters from fuzzy training data, and a fuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, resulting in a higher classification accuracy. Results of classifying a Landsat MSS image are presented, and their accuracy is analyzed. >

Fuzzy representation of geographical boundaries in GIS

Abstract Polygon boundaries on thematic maps are conventionally considered to be sharp lines representing abrupt changes of phenomena. However, in reality changes of environmental phenomena may also be partial or gradual. Indiscriminate use of sharp lines to represent different types of change creates a problem of boundary inaccuracy. Specifically, in the context of vector-based GIS, use of sharp lines to represent gradual or partial changes may cause misunderstanding of geographical information and reduce analysis accuracy. In this paper, the expressive inadequacy of the conventional vector boundary representation is examined. A more informative technique—the fuzzy representation of geographical boundaries—is proposed, in which boundaries describe not only the location but also the rate of change of environmental phenomena. Four methods of determining fuzzy boundary membership grades from different kinds of geographical data are described. An example of applying the fuzzy boundary technique to data analy...

Fuzzy information representation and processing in conventional GIS software: database design and application

Currently used methods for representing geographical information are inadequate because they do not tolerate imprecision. This leads to information loss and inaccuracy in analysis. Such expressive inadequacy is largely due to the underlying membership concept of classical set theory. To improve information processing in GIS research and application, an alternative membership concept is required. In this paper, we explore the inadequacy imposed upon geographical information representation by classical set theory and address the problems of information loss. A fuzzy relational data model is defined which is more representative for geographical information. A GIS database for agricultural land resource management is created by using the data model and a new technique for assessing land suitability is developed. The fuzzy representation largely facilitates data analysis in this GIS. The methods are tested with data from North Java, Indonesia using a vector-based GIS software package, Arc Info, and the analysi...

A knowledge-based system for highway network extraction

A knowledge-based system has been set up to extract highway networks automatically from Landsat thematic mapper (TM) imagery. The system was developed using image processing algorithms and some artificial intelligence techniques. It inputs raw TM data and outputs an interpreted image clearly identifying highway and road networks. The main algorithms in the system are discussed in detail, results are presented, and features of the system are examined. Landsat TM images of study areas in Toronto, Canada, are presented by way of illustration. >

A knowledge-based vision system for detecting land changes at urban fringes

An experimental vision system for remote sensing land use/cover change detection is presented. The system was designed to monitor the processes of urban development. The design strategy, system structure, and image analysis sequence are addressed. Some experimental results are presented. Techniques are described in detail which were aimed at addressing the key aspects to constructing a knowledge-based vision system: preprocessing, spatial representation and manipulation, and incorporating ancillary data. >

Towards a natural language user interface: an approach of fuzzy query

Abstract. A fuzzy query approach is presented which is aimed at introducing more natural language expressions into GIS user interfaces. In this approach “vague”natural language expressions are represented as fuzzy formulae and are processed in a systematic manner. This approach enables modification-type, composition-type and quantification-type fuzzy formulae to be used for data retrieval. This approach can be easily implemented, including on most of the existing nonfuzzy systems. The ease of implementation makes this approach attractive for developing new systems and for enhancing existing GIS. In this paper, the concepts underlying the approach and the methods for processing fuzzy formulae are described in detail. A prototype for implementing this approach and a group of sample queries are described.

A fuzzy grammar and possibility theory-based natural language user interface for spatial queries

Abstract A natural language interface can make a geographic information system (GIS) easier to use. It also allows one to query a GIS using vague or imprecise terms that are commonly used by end users. Major challenges in building a natural language interface include ambiguity and impreciseness. A natural language query may be ambiguous when it can be parsed in different ways. It may contain imprecise terms that have to be processed before an executable query can be generated. In this paper, we discuss the techniques we have developed, which are based on the possibility theory and fuzzy grammar theory, for handling ambiguity and impreciseness in natural language queries.

A parallel intersection algorithm for vector polygon overlay

A parallel intersection algorithm for speeding the creation of a composite network of polygons whose identities relate to the original polygons from which they are formed in vector geographic information systems (ISs) is described. The algorithm reduces unnecessary intersection checking, especially when polygon density is high. Single-stream and multiple-stream versions of the algorithm are discussed. Experimental results illustrating the overall performance of the parallel algorithm are presented.<<ETX>>

A space efficient XML DOM parser

In many XML applications, parsing is a key operation. When the processing involves modifying data, random access, and/or in an order different from the one in which elements are stored, a DOM parser has to be used. A major problem with using a DOM parser is memory consumption. The size of a DOM tree created from an XML document may be as large as 10 times of the size of the original document. Maintaining the tree of a big document requires a large amount of memory. It may cause costly swapping. In the worst cases, a DOM parser cannot handle a document at all because of its size. In this research, we develop a space efficient DOM parser, called SEDOM. It is based on a new compression approach and a set of manipulation algorithms, which enable many DOM operations to be performed when the data are in the compressed format, and allow individual parts of a document to be compressed, decompressed and manipulated. It can be used to efficiently manipulate very large XML documents. In this paper, we describe SEDOM, and compare its performance with three existing DOM parsers and an XML compressor.

Relational-linear quadtree approach for two-dimensional spatial representation and manipulation

An approach called relational-linear quadtree which provides information at multiple resolutions to describe spatial features together with descriptive or propositional information in desired structures is presented. It also provides powerful and flexible spatial analysis and manipulation ability. Its principle is quite clear, and implementation is very easy. It can be modified to fit other database models or structured knowledge representation schemes and can be implemented on most database management systems (DBMS) or expert system tools which support the structured models or schemes. The only extension is a set of quadtree operators which involve arithmetic operations on integers that can be coded as routines in a host language or a system command language. No additional processor is needed. This advantage makes the relational-linear quadtree a very practical approach in making use of the existing DBMSs or expert system tools to develop spatial information processing systems. >