CSC400W Honors Project Proposal
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CSC400W Honors Project Proposal
Understanding ocean surface features from satellite images
Jared TilanusNemanja Spasic
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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
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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
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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
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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
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Image Processing
• Working with the satellite images
• Make the computer recognize fronts– Position– Temperatures– Size
• Detect features – Eddies etc.
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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
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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
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Image Processing
• This section alone will be useful to Oceanographic researchers
• Accurate information about these features current status will be valuable for other research
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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