GI3020 Raster Data Modelling Syllabus:

GI3020 Raster Data Modelling Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 To inform the student of raster data model and the way to handle raster database.
 To educate the student about various statistical and numerical tools and techniques to handle image.
 To make student capable of handling images from various sources to extract basic information for application.

UNIT I POINT BASED FUNCTION

Image – properties – reading – color contrast – histogram – zooming and display –pan operations – pyramids – enhancements – transformation.

UNIT II NEIGHBORHOOD AND PROXIMITY ANALYSIS

8,4 D neighborhood – texture computation – GLCM – distance measurement – buffers (point,line,area).

UNIT III AREA DESCRIPTORS / BOUNDARIES

Area computation – shape numbers – perimeter – aspect ratio – point in polygon – line in polygon – overlay analysis.

UNIT IV MULTILAYER MODELING

Image ratio – indices – normalization – segmentation – similarity measures – maximum likelihood classification.

UNIT V STATISTICAL METRICS

Mean – mode – standard deviation – correlation –regression –covariance – kappa statistics – random sample selection.

TOTAL:45 PERIODS
COURSE OUTCOMES:

On completion of the course, the student is expected to
CO1 Acquaint with the raster data structure and its relevance in from pint based, neighbourhood based and region based geospatial .data analysis
CO2 Understand the various raster based data modeling applied on the earth observation data for resource management
CO3 Evaluate the procedures of spatial data handling using raster data model for solving resource management problems
CO4 Acquire knowledge on the current development, issues methods and solutions in raster data analysis using earth observation data.
CO5 Analyze critically and evaluate methods by applying the knowledge gained and to be a part of innovation and integration of geospatial data modeling

TEXTBOOKS:

1. Geoprocessing with python, Chris Garrad, First edition, 2016
2. Python Data Analysis Cookbook, 2016.
3. Learning Geospatial Analysis with Python, Joel Lawhead, Third edition, 2019.
4. Digital Image Processing using MATLAB, Rafael C. Gonzalez, Third edition, 2020.

REFERENCES

1. Digital Image Processing, Rafael C. Gonzalez, Richard E. Woods , Pearson , Fourth edition, 2022.
2. Concepts and Techniques in Geographic Information System, C.P Lo., Second Edition, 2016.
3. Deep Learning with Python, Manning, Second Edition, 2021.
4. Geocomputation with R, Jakub Nowosad, JannesMuenchow, and Robin Lovelace, First Edition, 2020.
5. Advance Custom Raster Processing using Python, Jie Zhang, Hao. Available from:
Hu.https://www.esri.com/content/dam/esrisites/en-us/events/conferences/2020/developersummit/advance-raster-processing-using-python.pdf