Raster Database

21
Group 3 Akash Agrawal and Atanu Roy 1 Raster Database

description

Raster Database. Group 3 Akash Agrawal and Atanu Roy. 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. Learning Objectives. Learning Objectives (LO) - PowerPoint PPT Presentation

Transcript of Raster Database

Page 1: Raster Database

Group 3 Akash Agrawal and Atanu Roy

1

Raster Database

Page 2: 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

2

Page 3: Raster Database

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

3

Page 4: Raster Database

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.

4

Page 5: Raster Database

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

5

Page 6: Raster Database

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

6

Page 7: Raster Database

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

7

Page 8: Raster Database

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.

8

Page 9: Raster Database

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

9

Page 10: Raster Database

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

10

Page 11: Raster Database

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.

11

Page 12: Raster Database

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

Page 13: Raster Database

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.

13

Figure: An example of focal operation. (a) Rook neighborehood. (b) Bishop neighborehood. (c) Queen neighborehood. (d) Focal sum using queen neighborehood.

Page 14: Raster Database

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.

14

Page 15: Raster Database

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.

Page 16: Raster Database

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

16

Page 17: Raster Database

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.

17

Page 18: Raster Database

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

18

Page 19: Raster Database

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

19

Page 20: Raster Database

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.

20

Page 21: Raster Database

21