CSC400W Honors Project Proposal

Understanding ocean surface features from satellite images

Jared TilanusNemanja Spasic

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

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

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

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

Image Processing

• Working with the satellite images

• Make the computer recognize fronts– Position– Temperatures– Size

• Detect features – Eddies etc.

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

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

Image Processing

• This section alone will be useful to Oceanographic researchers

• Accurate information about these features current status will be valuable for other research

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

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

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

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

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

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

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.

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

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

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

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