Group 3 Akash Agrawal and Atanu Roy 1 Raster Database.
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Transcript of Group 3 Akash Agrawal and Atanu Roy 1 Raster Database.
Group 3 Akash Agrawal and Atanu Roy
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Raster Database
Chapter Organization
• 1.1 Raster Data• 1.2 Raster Data in GIS
– 1.2.1 Spatio-Temporal Data– 1.2.2 Field Operations– 1.2.3 Storage– 1.2.4 Retrieval Techniques
• 1.3 Concluding Remarks
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Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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Raster Data
• A raster image is rows and columns of cells organized in a rectangular grid.• Each cell is called a Pixel.• Each pixel stores a singular color/attribute value.• Resolution of rater image is denoted by #pixels in row X #column of the grid.
– 800X600 resolution denotes that the raster image contains 600 rows of 800 pixel each.
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Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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Raster Data in GIS
• The primary purpose is to display the detailed image on a map area or render its identifiable objects by digitization.
• Raster maps are ideally suited for mathematical modeling and quantitative analysis.
• Data storage techniques data are easy to program and gives good performance for data retrieval.
• Commonly used form of raster data in the field of GIS – aerial photographs of some area.
• Other raster datasets used in GIS– a digital elevation model– Map of reflectance of a particular wavelength of light.– Landsat– Electromagnetic spectrum indicators
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Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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How Spatio-Temporal data is represented?
• The ST data has become crucial – to understand cause and effect scenarios– development of dynamic models for the analysis of it.
• The Snapshot Model– Every layer in the snapshot model shows the state of geographic distribution
at one time stamp. – Time intervals between any two layers may vary– There is no explicit implication for changes within the time lag of any two
layers.
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Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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Field data
• Field data are an essential part of GIS systems.– give most up-to-date information about current events– Needed for creating/updating digital maps– Help in validating the available data sets.
• Field data source– Satellites– Geo-registered sensor networks etc.
• Field data set example– Satellite images, aerial photographs– Digitized paper maps– Earth Science data-sets, e.g. rainfall, temperature maps
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Field operations
• Field data can be manipulated using– Map algebra– Image algebra
• Map algebra vs. Image algebra– Similarity:
• Operand: raster data
– Difference:• Image algebra deals with image properties such as color information, number of
pixel, pixel size etc. Example trim/crop, zoom in/out etc.• Map algebra deals with attribute maps such as temperature map, vegetation map
etc. Example thresholding, gradient etc.
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Map Algebra
• Map algebra– Operand: raster data– Operation: classified in four groups
• Local, focal, global and zonal
• Local operation: – The value of a cell in the new raster is computed only using the value of that cell in
the original raster. – Example thresholding, point wise addition etc.
12Figure: An example thresholding with threshold value of 4
Map Algebra (Cont…)
• Focal operation: – The value of a cell in the new raster is computed using the value of that cell
and its neighboring cells in the original raster. – Example focal sum, gradient etc.
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Figure: An example of focal operation. (a) Rook neighborehood. (b) Bishop neighborehood. (c) Queen neighborehood. (d) Focal sum using queen neighborehood.
Map Algebra (Cont…)
• Global operation:– The value of a cell in the new raster is computed using the location or values
of all cells in the original raster data.– Example: global sum, global average etc.
• Zonal operation– the value of a cell in the new raster is a function of the value of that cell in the
original raster and the values of other cells which appear in the same zone specified in another raster.
– Example distance from nearest facility.
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Image Algebra
• Map algebra– Operand: raster data/ Image– Operation:
• ignores the absolute location of pixels.• come from image processing literature.• used for display or rendering the image for manual analysis of demonstration
purpose.• Example: trim/crop, zoom in/out, rotate etc.
15Figure: An example trim operation.
Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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Storage Techniques
• Traditional Approach– standard file-based structure of TIF, JPEG, etc.– use custom software to retrieve data-items of interest– Pros: provide good compression and require less storage space.– Cons: difficult to index the data and hence has slower retrieval operation.
• Database Approach– stores the raster data items attributes such as geo-location, time-stamp,
various properties etc. in database tables.– Use database query language such as SQL to retrieve data-item of interest.– Pros:
• allows quicker retrieval of the raster data.• allows user defined attributes and support for ad-hoc queries.
– Cons: require storage of millions of significantly sized records.
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Learning Objectives
• Learning Objectives (LO)– LO1 : Learn about Raster Data– LO2 : Learn about GIS Raster Database
• Why use Raster data in GIS?• How Spatio-temporal data is represented?• What are different Field operations?• What are different Storage techniques?• What are different Retrieval Techniques?
• Mapping Sections to learning objectives– LO1 - 1.1– LO2 - 1.2
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Retrieval Techniques• Raster data sets are very rich in content• Retrieval approaches
– Meta-data approach (database approach)– Content based retrieval (image processing technique)
• Meta-data approach– stores values of descriptive attributes for each raster data item.– uses simpler SQL data types such as numeric, string, date etc.– queries to select a set of descriptive attributes such as location, time-stamp,
subject etc.– Pros:
• Simpler to implement• gives accurate answers for queries to select a set of descriptive attributes.
– Cons:• Queries are limited to descriptive attributes.• does not support “similarity” based queries
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Retrieval Techniques (Cont…)• Content based retrieval or content based image retrieval (CBIR)
– content of an image is represented by extracted primitive visual features such as representing color, shape and texture.
– Similar image queries are answered based on some combination of these primitive features.
– CBIR is a two step approach• Step 1: compute a feature vector or attribute relation graph (ARG) for each image
in the database.• Step 2: given a query image, compute its ARG and compare to the ARGs in the
database for the image most similar to the query image.
– The success of this approach depends on efficiency of feature and similarity measure, used to compare two ARGs.
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