Transcript
Semi-automated penguin countingfrom digital aerial photographs
S.J McNeill K Barton P Lyver D Pairman
Landcare Research New Zealand
Motivation
Understanding changes in penguin population is important,as these can be used as indicators of anthropogenic andfoodweb e�ects
Aerial photography is used in the Ross Sea (Antarctica) tocapture a reliable count of Adélie nesting penguins
From 1981, the Ross Sea area (158o�175o E) has beensurveyed annually
There are many di�culties in achieving this census count:
Timing is critical,Ground counting is di�cult or impossible,Counting using prints is di�cult to control and validate.
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Objectives
Determine if it is possibleto reliably detect Adéliebreeding penguins inimages
Generate software to(semi-)automate thecensus process.
Test, using an �expert�,and optimise interactivity.
Adult Adélie penguin (Pygoscelis adeliae)
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Adélie penguins
The most abundant and widespread Antarctic penguin
10 million Adélie make up 80% of the Southern Ocean birdbiomass
38% of all Adélie penguins are found in the Ross Sea
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Image capture
Images captured using a hand-held camera through the opendoors of a helicopter and/or C-130 Hercules
Hasselblad H1D with a Phase One digital camera back
Image size 5440× 4080, 3-bands natural colour, TIFFEXIF data provides date/time and aperture informationTypical ground resolution better than 0.5 m
Ten representative images were selected for analysis
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Full-scene example
5440× 4080 full-scene
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Sub-scene example
870× 510 sub-scene
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Analysis
Human detection of breeding Adélie not straightforward
There are many similar-looking objects in the imagesProposed revised approach:
Detect the distinctive area of the colonyOnly count penguins within colony areaProvide software features to easily add/delete penguins
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Colony/background discrimination
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Colony & penguin detection
Background is largely monochromatic
Colony area covered in guano and has a red excess overgreen or blue, with higher saturation
Use linear discriminant analysis to separate colony frombackground, based on:
Natural colour counts (RGB) converted to hue, saturation,lightness (HSL) space values,Two-way interactions of HSL space values,Aperture setting.
Classi�cation followed by morphological opening and closingde�ne the colony area
Penguins detected as dark local minima within colony area
Penguin �objects� pruned to upper threshold of circularityP2/ (4πA) to remove long thin objects
Adopt the centroid of the surviving objects as penguins
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Original image (350× 250)
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Detected colony
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Cleaned colony
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Candidate penguin locations
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Overlaid penguins
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Editing facilties
Detection procedure does not count all �real� penguins
False penguins counted
Non-breeding penguins within colonyPenguin shadows or spurious dark objects
True penguins missed
Breeding penguins outside colonyPenguins indistinct compared to surroundings
Editing facilities required:
Overlap between photographs requires group deletionsAdd or delete individual penguinsCheck that penguins are not double-countedRecord of editing steps maintainedNumber of editing steps requires �single-click� operation
IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Implementation
Software written in Matlab 2010b, deployed with Matlabcompiler
Census results stored for each captured image in a small �le
Deployed for testing phase to a penguin ecologist
Second development phase to �x faults and improveinteractive response:
Reduce memory overhead for each counted penguinReduce keystroke e�ort for additions/deletionsAdd ability to count penguins within non-guano stained area
No problems reported after second phase deployment
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Editing software
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Bootstrap colony classi�cation rates
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Colony classi�cation rates
Accurate colony delineation is very important
Requirement is for high true positive, low false negative rates
About 5% of images give poor results:
Due to very poor colony/background distinctionNo clear reason for this poor result
CF001669 CF001720
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Conclusions
Semi-automated penguin counting is a pragmatic approach
Laborious counting automated; �ne editing left for an expert
Software allows editing, maintains counts, stores results
Emphasis is interactive productivity
Acknowledgements
Ministry for Science and Innovation (funding).Antarctica New Zealand (funding and logistics).
Helicopters New Zealand (�ying).Squadron 40, Royal New Zealand Air Force (�ying).
IGARSS-2011, 25-29 July 2011, Vancouver, Canada