Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session F-2 WG II/3 - Technologies for Large-Volumes of Spatial Data - Part B]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: LARGE SPATIAL OBJECT HANDLING IN GEOGRAPHIC INFORMATION SYSTEMS Wenjin Zhou

Document type:: Monograph

Structure type:: Chapter

difficult to retrieve an image by its name or number(or other identifiers), but it may be for more difficult to do it through selection criteria that would be contained in the image. This may lead to very long research, or even sometimes to tedious image processing (e.g. for similarity retrieval). This observation leads us to consider a more powerful data model and processing methods that can be used to handle large objects. 3. LARGE SPATIAL OBJECT QUERY PATTERNS Before we discuss new data model and processing methods that can be used to improve the system performance on large objects handling, we need to examine how users will make use of large spatial objects in GISs. First, many queries are concerned about the general information of large objects and these can be classified as requests of meta-data, i.e. information about large objects. The common characteristics of this type of queries are that queries touch a high volume of data, but the generated answers are tiny by comparison. With our model, it is not difficult to figure out that this type of queries can be easily answered using DAD or DRAD. The second type of query is a kind of data mining. Users try to query over a large set of objects in order to find a small set of specific data. For example, a user may want to find an image that has a certain percentage of the snow coverage. This type of queries does not require the exact and detailed information, but instead requires the "overview" of the special nature of a large object. In this case, low resolution images with certain rules specified in a query language to derive the required results are possible solution for this purpose. The third type of query displays a large spatial object on a computer screen in order to do image interpretation or image analyses. Due to the small size of computer screen, only a very tiny part of the large object can be displayed on the screen at a time with the original data resolution. In this case we can significantly improve the system performance by windowing the image first and then only render the small part of data on the screen. By buffering the nearby part of the image, we can also perform Zoom/pan in a very fast fashion. This can be very significant when we compare to the case where the whole image data needs to be read from a data base. The fourth type of query is data browsing. Users wish to perform data browsing because they are not sure which type of data they are dealing with. Class hierarchy or data base schema browsing can be very helpful to the new users. Useful summary information includes histograms showing data statistical feature, images outline showing the data extent and well-designed icons showing data characteristics. For GIS applications it is our belief that all these techniques are critical to the success of GISs when they are used to handle large spatial objects. 4. OBJECT ORIENTED DATA MODEL Before we present our new data model, let us consider a simple example of a large image object. The image is a Landsat image with DRAD such as image dimension, the geographical location of the image origin, the band number, the pixel precision, the acquisition time, the control points used for image geometric rectification, the parameters used for radiation correction, the data quality, etc.. The DAD data for the Landsat image may be its histogram, classification result(a set of polygons with category numbers), a two dimensional array for the classification precision, a set of rules to derive a certain theme (for example, a rule like: "if a pixel value is large than 10 and less than 20, then the pixel can be classified as water".) It is difficult if not possible for the relational data model to handle this non primitive data (e.g. point, polygon, array), rules and data operation methods. However, they can be well handle by an object oriented data model [Zhou and Wilkinson, 1993]. In the above example, we could define an object class Landsat image, with various attributes to store meta-data, with methods to present the correction models, and with class rules to present the data classification rules[Hughes, 1993]. Two most often used methods to handle large objects in object oriented models are: first, the extended relational DBMSs use Abstract Data Types(ADTs) to support large and unstructured data such as images[Stonebraker, 1993]. It allows users to define their own functions to manipulate the ADTs. This method allows the system to be extended, but leaves the extensions to its users. In addition, the raw data is treated as unformatted byte strings and very little semantic information is maintained in the system. The second method stores large objects in user defined classes, and allows the data base administrator to define methods and other attributes for classes[Deux et al., 1991]. Usually both methods store raw data using Binary Large OBjects(BLOB), and provide the capability to store and retrieve them through a query language. In both methods the row data is treated as a whole 214 and no users. ] generall extende Therefor users mi and clus manage or opera To mak environ compre redunda investig; our dal patterns method strategi Using t implem help us schema] the data 5.L Many ci to effect attribut: as remo DTM ai with v charact: often re answer inform: queries concerr objects r In orde: and ef applica Spatial devices traditio: imagen out lir dimens be opti object - current 1000 by map) a only a

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