CSC400W Honors Project Proposal

1. CSC400W Honors Project Proposal Understanding ocean surface features from satellite images Jared Tilanus Nemanja Spasic

2. Project Background • Project Supervisor: Dr. Anet Potgieter • Proposed by Mr. Laurent Drapeau, member of the French company De l’Institut de Recherche pour le Développement , and Prof. J. Field of the UCT Oceanography department • Mr. Drapeau’s company is comparing the ocean features of South Africa and South America • Prof. J. Field has a lot of oceanographic data that he needs visual representations for

4. Understanding ocean surface features from satellite images • Develop a system to automatically detect features from thermal images • Fronts (where cold and warm water meet) • Eddies • Upwelling • Gather information about these features • Important to the study of the ocean as these features determine lots about ocean life

5. Understanding ocean surface features from satellite images • Our system will give quantitative information on current conditions • System also aims to detect patterns in how these features occur • Seasonal averages • Seasonally persistent features • Predict how features evolve

6. Understanding ocean surface features from satellite images • Jared will do develop image processing software to detect (and possibly identify) features from the original images • Nemanja will develop a Bayesian network to identify features and recognise patterns

7. Image Processing • Working with the satellite images • Make the computer recognize fronts • Position • Temperatures • Size • Detect features • Eddies etc.

8. Image Processing • Essentially edge detection, segmentation and feature recognition • Many algorithms exist • My project is to select ones that will work on the noisy data we have and implement them • Algorithms need to be tuned to work optimally

9. Image Processing • Data is noisy by nature and incomplete • Features are messy and hard to distinguish exactly • Areas are often covered by cloud • Will probably use an algorithm that tracks features across multiple images • Eliminates some noise • Temporal changes are clearer

10. Image Processing • This section alone will be useful to Oceanographic researchers • Accurate information about these features current status will be valuable for other research

11. Image Processing • Success of this section will be best evaluated by eye • By overlaying detected features on the original images one will be able to see how effective the software is

12. Output Format • Will be a challenge representing data that is output • Initially will probably be stored in some XML format • Perhaps topic maps • Would be useful to represent it as an image • Easy to see lots at once

13. Output Format • Difficult to represent temporal information in an image • Will do user requirements gathering to see what information is important • Will evaluate intuitiveness and informativeness on users • Expert and non-expert

14. Bayesian networks summary • A directed acyclic graph (DAG) • Consists of a set of nodes: variables or uncertain quantities • Nodes are linked by directional arcs , where the parent node is the cause and the child node is the effect • Links represent informational or casual dependencies among nodes, which are given in terms of conditional probabilities • Each variable has a finite set of mutually exclusive propositions - states

15. Bayesian networks summary 2 • Bayesian networks can be singly-connected (without loops) or multiply-connected (loops) • A Dynamic Bayesian network handles varying values for each variable over a time period and is probably best suited to the project

16. Bayesian network software • Open source software will be used initially to learn how to use a Bayesian network • Potential software would be : BayesiaLab and Bayesian network tool in java – BNJ • Available open source packages are very slow to train and do not handle temporal data patterns

17. Temporal Bayesian Inference • The data we will have access to is temporal and thus software will have to be designed to allow the Bayesian network to handle temporal data • Dr. A. Potgieter has algorithms that can be used to develop software for temporal data inference by a Bayesian network • Research will have to be extensively done to design the required software.

18. Bayesian network data input interface • A user friendly interface will be designed to enable quick, efficient and easy entry of data into the Bayesian network • User Centered Design will be used to accomplish the use friendly interface goal. • Probable software for implementing the user interface would be visual c++, visual j++ builder of Flash MX

19. Output visualization • The output of the Bayesian network will probably be stored in xml or topic map format • The stored output data will probably be converted to a bmp format to allow most graphical software packages to open them and • bmp format is a binary rasta (pixel based) format so it is easy to work with

20. Project Benefits • Beneficial to research being done by Mr. Drapeau • Beneficial to the UCT oceanographic department as they will have visual representations of their data • Allow researchers to easily access information contained in thermal images of the ocean surface • Beneficial to local fishermen as they will be able to detect which ocean surface patterns attract the most fish • May be used by a person studying migration of fish to determine which ocean feature makes fish migrate

21. Project Successfulness • Comparing the output data of the Bayesian network and the input satellite images will give a clear indication of the success of the prediction and inference of the Bayesian network • Comparison to an existing Oceanographic model will also be used as a success rating • A non-experts opinion of the final output visual representation will give a good idea of the projects visual representation success