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

tigated is al DBMS and does he data. In trinsically 'S richer. tem before vely and AD in our e, because built into processing ' indexing queries in rocess of 1lection of raction of s created analyses, elligence. ostraction. )deling is fication is y may be juery may 'hat is the ery easily le. While orm data only limit a and the jects. For lerive the arameters ssification ; such as al editing >sentation odeled as base for ability to al DBMS >, one can ; indexed, kind of is. Value aditional applications, but will not work for the type of large objects such as remotely sensed data. First, queries are issued to retrieve images by their content, e.g. to find all images that have Lake Ontario. This search requires indexes on the result of a classification function (on virtual attributes) and not on raw images. Second, indexing functions for images and text often return a collection of values for which efficient access is desired. For example, a keyword extraction function might return a set of relevant keywords for a document, and the user desires indexing on all keywords. To be able to retrieve large object stored in databases we must associate identifying parameters with each of the large objects. It is through these parameters that we can select stored large objects for display, comparison, queries and further analysis. Primary parameters are produced when large objects are obtained and describe the large object's acquisition procedures and it's related properties. For fast systematic analyses and access we need to retrieve images based upon their contents. The pixels that make the image are rarely suitable for direct search. If we can provide secondary parameters indicating the location and type of objects seen on the image, the database searches could be enabled that directly serve the end-user’ objectives. Modern GISs without such a retrieval capability will not be justifiable. However, the majority of secondary parameters used today, when available at all, is entered by humans after visually scanning an image [Wiederhol 1989 and Chang 1992], This observation motivates us to use abstraction techniques to generate the secondary parameters. Although a completely automated abstraction is our long term goal, only semi-automatic analysis and classification are used in this research to generate the DAD that is used as secondary parameters in the content based queries. 6. PERFORMANCE EVALUATION Before we look into our examples, let us first consider how big some typical spatial objects may be in GISs: a) a scanned 8 bits map data 10, 000 by 10,000 pixels without compression is about 100 MB; b) a SPOT panchromatic image 6000 by 6000 is about 36 MB; ¢) a Landsat Thematic frame(7 bands) is about 300 MB; With more than 1000 large objects in a GIS database, we can imagine how important it is to effectively and efficiently store, transfer and process these large objects. The methods used to handle these large spatial objects will mainly decide the success of GISs. 6.1 Application Examples Without practical examples with real GIS queries to large spatial objects, we can only speculate on the efficiency of the methods we presented. In the previous section, we have analyzed types of queries a user may actually run against large spatial objects and shown how often the abstracted data can be used to answer user's queries. In this section we will demonstrate the difference in system performance when our approaches are used. To show the performance improvement using our proposed methods, we use a data base that consists of ten large image objects(about 50 MB), 5 MB vector and attribute data(including DRAD and DAD). We consider this database very small compared to real GIS data bases. This database is stored in the disk on a SUN SPARC 2 station with 32 MB memory. We post the following five different types of queries on large spatial objects in the database: 1) Overview: display an image in a window; 2) Zoom/pan: Zoom/pan an image. 3) Thematic selections: Show me the percentage of "Water Coverage" in a large image object. 4) Range Query: Show the image that is contained in a polygon ABCD. 5) Automatic analysis: Detect all edges in a Landsat image using a predefined method" These selections are often followed by further processing steps in practical applications to refine the selections. This is an important feature in large object queries. We will not discuss this in this paper because this refinement is very specific for each particular application. 6.2 Query Cost Comparisons a) Query One: Image Overview Using the traditional method, we would need to read all image data from the database into memory, zoom out the image to make it fit in the window and then display image data on the window. This will take several minutes to complete. In the proposed method, we just retrieve the overview image that is generated using lower data quantization and spatial resolution at the time 217

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