Semi-automated penguin counting from digital aerial photographs S.J McNeill   K Barton     P Lyver       D Pairman        ...
Motivation        Understanding changes in penguin population is important,        as these can be used as indicators of a...
Objectives        Determine if it is possible        to reliably detect Adélie        breeding penguins in        images  ...
Adélie penguins        The most abundant and widespread Antarctic penguin        10 million Adélie make up 80% of the Sout...
Image capture        Images captured using a hand-held camera through the open        doors of a helicopter and/or C-130 H...
Full-scene example 5440   × 4080    full-sceneIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Sub-scene example 870   × 510   sub-sceneIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Analysis        Human detection of breeding Adélie not straightforward        There are many similar-looking objects in th...
Colony/background discriminationIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Colony  penguin detection        Background is largely monochromatic        Colony area covered in guano and has a red exc...
Original image (350 × 250)IGARSS-2011, 25-29 July 2011, Vancouver, Canada
Detected colonyIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Cleaned colonyIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Candidate penguin locationsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Overlaid penguinsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Editing facilties        Detection procedure does not count all real penguins        False penguins counted               ...
Implementation        Software written in Matlab 2010b, deployed with Matlab        compiler        Census results stored ...
Editing softwareIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Bootstrap colony classication ratesIGARSS-2011, 25-29 July 2011, Vancouver, Canada
Colony classication rates        Accurate colony delineation is very important        Requirement is for high true positiv...
Conclusions        Semi-automated penguin counting is a pragmatic approach        Laborious counting automated; ne editing...
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mcneills_igarss2011_penguins.pdf

  1. 1. Semi-automated penguin counting from digital aerial photographs S.J McNeill K Barton P Lyver D Pairman Landcare Research New Zealand
  2. 2. Motivation Understanding changes in penguin population is important, as these can be used as indicators of anthropogenic and foodweb eects Aerial photography is used in the Ross Sea (Antarctica) to capture a reliable count of Adélie nesting penguins From 1981, the Ross Sea area (158 175 o o E) has been surveyed annually There are many diculties in achieving this census count: Timing is critical, Ground counting is dicult or impossible, Counting using prints is dicult to control and validate.IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  3. 3. Objectives Determine if it is possible to reliably detect Adélie breeding penguins in images Generate software to (semi-)automate the census process. Test, using an expert, and optimise interactivity. Pygoscelis adeliae) Adult Adélie penguin (IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  4. 4. Adélie penguins The most abundant and widespread Antarctic penguin 10 million Adélie make up 80% of the Southern Ocean bird biomass 38% of all Adélie penguins are found in the Ross SeaIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  5. 5. Image capture Images captured using a hand-held camera through the open doors 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, TIFF EXIF data provides date/time and aperture information Typical ground resolution better than 0.5 m Ten representative images were selected for analysisIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  6. 6. Full-scene example 5440 × 4080 full-sceneIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  7. 7. Sub-scene example 870 × 510 sub-sceneIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  8. 8. Analysis Human detection of breeding Adélie not straightforward There are many similar-looking objects in the images Proposed revised approach: Detect the distinctive area of the colony Only count penguins within colony area Provide software features to easily add/delete penguinsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  9. 9. Colony/background discriminationIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  10. 10. Colony penguin detection Background is largely monochromatic Colony area covered in guano and has a red excess over green or blue, with higher saturation Use linear discriminant analysis to separate colony from background, based on: Natural colour counts (RGB) converted to hue, saturation, lightness (HSL) space values, Two-way interactions of HSL space values, Aperture setting. Classication followed by morphological opening and closing dene the colony area Penguins detected as dark local minima within colony area Penguin objects pruned to upper threshold of circularity P 2 / (4πA) to remove long thin objects Adopt the centroid of the surviving objects as penguinsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  11. 11. Original image (350 × 250)IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  12. 12. Detected colonyIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  13. 13. Cleaned colonyIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  14. 14. Candidate penguin locationsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  15. 15. Overlaid penguinsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  16. 16. Editing facilties Detection procedure does not count all real penguins False penguins counted Non-breeding penguins within colony Penguin shadows or spurious dark objects True penguins missed Breeding penguins outside colony Penguins indistinct compared to surroundings Editing facilities required: Overlap between photographs requires group deletions Add or delete individual penguins Check that penguins are not double-counted Record of editing steps maintained Number of editing steps requires single-click operationIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  17. 17. Implementation Software written in Matlab 2010b, deployed with Matlab compiler 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 improve interactive response: Reduce memory overhead for each counted penguin Reduce keystroke eort for additions/deletions Add ability to count penguins within non-guano stained area No problems reported after second phase deploymentIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  18. 18. Editing softwareIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  19. 19. Bootstrap colony classication ratesIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  20. 20. Colony classication 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 distinction No clear reason for this poor result CF001669 CF001720IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  21. 21. 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
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