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Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

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Abstract

Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for glare-free data collection. To build this model, we leverage a small labelled dataset to perform semi-supervised learning. The large dataset is labelled with a Cascading Random Forest Model using a naïve pseudo-labelling approach. A reflectance model is used, which pinpoints features of interest, to populate our datasets which allows for context-aware machine learning models. The pseudo-labelled dataset is used on two models: a Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay the foundation for data-driven mission planning; a glare modelling system which allows surveyors to preemptively minimize glare and reduces survey reliance on the detection function as an estimator of whale populations during periods of poor subsurface visibility.

Supported by National Research Council Canada.

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References

  1. Akashi, Y., Okatani, T.: Separation of reflection components by sparse non-negative matrix factorization. Comput. Vis. Image Underst. 146 (2015). https://doi.org/10.1016/j.cviu.2015.09.001

  2. Alzahrani, A., Kimball, J.W., Dagli, C.: Predicting solar irradiance using time series neural networks. Procedia Comput. Sci. 36, 623–628 (2014). https://doi.org/10.1016/j.procs.2014.09.065

    Article  Google Scholar 

  3. Arazo, E., Ortego, D., Albert, P., O’Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

    Google Scholar 

  4. Artusi, A., Banterle, F., Chetverikov, D.: A survey of specularity removal methods. Comput. Graph. Forum 30(8), 2208–2230 (2011). https://doi.org/10.1111/j.1467-8659.2011.01971.x

    Article  Google Scholar 

  5. Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: MixMatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)

    Google Scholar 

  6. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L., Thomas, L.: Introduction to distance sampling: estimating abundance of biological populations. Technical report, Oxford (United Kingdom) Oxford University Press (2001)

    Google Scholar 

  7. Buckland, S.T., Anderson, D.R., Burnham, K.P., Laake, J.L., Borchers, D.L., Thomas, L.: Advanced Distance Sampling: Estimating Abundance of Biological Populations. OUP, Oxford (2004)

    Google Scholar 

  8. Cox, C., Munk, W.: Measurement of the roughness of the sea surface from photographs of the sun’s glitter. J. Opt. Soc. Am. 44(11), 838–850 (1954). https://doi.org/10.1364/JOSA.44.000838

    Article  Google Scholar 

  9. Foley, J.D., van Dam, A., Feiner, S.K., Hughes, J.F.: Computer Graphics: Principles and Practice, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1990)

    MATH  Google Scholar 

  10. Gosselin, J.F., Lawson, J., Ratelle, S.: DFO Science Twin Otter Marine Mammal Surveys: Unprocessed aircraft imagery and aerial tracks (2018)

    Google Scholar 

  11. Hersbach, H., et al.: ERA5 hourly data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). https://doi.org/10.24381/cds.adbb2d47

  12. Hunter, J.D.: Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007). https://doi.org/10.1109/MCSE.2007.55

    Article  Google Scholar 

  13. Khan, H.A., Thomas, J.-B., Hardeberg, J.Y.: Analytical survey of highlight detection in color and spectral images. In: Bianco, S., Schettini, R., Trémeau, A., Tominaga, S. (eds.) CCIW 2017. LNCS, vol. 10213, pp. 197–208. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56010-6_17

    Chapter  Google Scholar 

  14. Klinker, G., Shafer, S., Kanade, T.: The measurement of highlights in color images. Int. J. Comput. Vision 1(1), 7–32 (1988)

    Article  Google Scholar 

  15. Lawson, J.W., Gosselin, J.F.: Distribution and Preliminary Abundance Estimates for Cetaceans Seen during Canada’s Marine Megafauna Survey-a Component of the 2007 TNASS. Fisheries and Oceans Canada, Science (2009)

    Google Scholar 

  16. Mardaljevic, J., Andersen, M., Roy, N., Christoffersen, J.: Daylighting metrics: is there a relation between useful daylight illuminance and daylight glare probabilty? In: Proceedings of the Building Simulation and Optimization Conference BSO12. No. CONF (2012)

    Google Scholar 

  17. Marques, F.F., Buckland, S.T.: Incorporating covariates into standard line transect analyses. Biometrics 59(4), 924–935 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  18. Marques, T.A., Thomas, L., Fancy, S.G., Buckland, S.T.: Improving estimates of bird density using multiple-covariate distance sampling. Auk 124(4), 1229–1243 (2007)

    Article  Google Scholar 

  19. Mobley, B.: Light and Water: Radiative Transfer in Natural Waters. Academic Press, San Diego (1994)

    Google Scholar 

  20. Mobley, C.D.: The Optical Properties of Water In Handbook of Optics, 2nd edn. McGraw-Hill, New York (1995)

    Google Scholar 

  21. Mobley, C.D.: Estimation of the remote-sensing reflectance from above-surface measurements. Appl. Opt. 38(36), 7442–7455 (1999)

    Article  Google Scholar 

  22. Morel, A., Voss, K.J., Gentili, B.: Bidirectional reflectance of oceanic waters: a comparison of modeled and measured upward radiance fields. J. Geophys. Res. Oceans 100(C7), 13143–13150 (1995). https://doi.org/10.1029/95JC00531. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/95JC00531

  23. Power, J., Drouin, M.A., Durand, G., Thompson, E., Ratelle, S.: Classifying glare intensity in airborne imagery acquired during marine megafauna survey. In: OCEANS 2021: San Diego – Porto, pp. 1–7 (2021). https://doi.org/10.23919/OCEANS44145.2021.9705752

  24. Power, J., et al.: Real-time mission planning simulations from geospatial data. In: 2021 IEEE/ACM 25th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 1–2 (2021). https://doi.org/10.1109/DS-RT52167.2021.9576133

  25. Shafer, S.: Using color to separate reflection components. Color. Res. Appl. 10(4), 210–218 (1985)

    Article  Google Scholar 

  26. Shen, H.L., Zhang, H.G., Shao, S.J., Xin, J.: Chromaticity separation of reflection component in single images. Pattern Recogn. 41, 2461–2469 (2008). https://doi.org/10.1016/j.patcog.2008.01.026

    Article  MATH  Google Scholar 

  27. Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306

    Article  MathSciNet  MATH  Google Scholar 

  28. Shi, W., Gong, Y., Ding, C., Tao, Z.M., Zheng, N.: Transductive semi-supervised deep learning using min-max features. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 299–315 (2018)

    Google Scholar 

  29. Soulayman, S.: Comments on solar azimuth angle. Renew. Energy 123, 294–300 (2018)

    Article  Google Scholar 

  30. Suo, J., An, D., Ji, X., Wang, H., Dai, Q.: Fast and high quality highlight removal from a single image. IEEE Trans. Image Process. 25(11), 5441–5454 (2016). https://doi.org/10.1109/TIP.2016.2605002

    Article  MathSciNet  MATH  Google Scholar 

  31. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  32. Wagdy, A., Garcia-Hansen, V., Isoardi, G., Allan, A.: Multi-region contrast method -a new framework for post-processing HDRI luminance information for visual discomfort analysis. In: Proceedings of the Passive and Low Energy Architecture Conference 2017: Design to Thrive – Foundations for a Better Future (2017)

    Google Scholar 

  33. Wojtkiewicz, J., Hosseini, M., Gottumukkala, R., Chambers, T.L.: Hour-ahead solar irradiance forecasting using multivariate gated recurrent units. Energies 12(21), 4055 (2019). https://doi.org/10.3390/en12214055

    Article  Google Scholar 

  34. Yang, J., Liu, L., Li, S.Z.: Separating specular and diffuse reflection components in the HSI color space. In: 2013 IEEE International Conference on Computer Vision Workshops, pp. 891–898 (2013). https://doi.org/10.1109/ICCVW.2013.122

  35. Ye, S., Yin, J.L., Chen, B.H., Chen, D., Wu, Y.: Single image glare removal using deep convolutional networks. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 201–205 (2020). https://doi.org/10.1109/ICIP40778.2020.9190712

  36. Zimmerman-Moreno, G., Greenspan, H.: Automatic detection of specular reflections in uterine cervix images. In: Reinhardt, J.M., Pluim, J.P.W. (eds.) Medical Imaging 2006: Image Processing, vol. 6144, pp. 2037–2045. SPIE (2006). https://doi.org/10.1117/12.653089

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Acknowledgements

The authors would like to thank Mylene Dufour, Marie-France Robichaud, and Maddison Proudfoot for their dedicated work in labelling and preparing the FIT dataset used in our experiments. Stephanie Ratelle provided us with valuable insight on the challenges associated with megafauna surveys. The authors would also like to thank both Stephanie and Elizabeth Thompson for their crucial role in promptly granting us access to the CAM3 dataset and its associated flight tracks. Their experience in conducting systematic aerial surveys throughout eastern Canadian waters was essential to our work.

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Authors and Affiliations

  1. University of New Brunswick, Fredericton, NB, E3B 5A3, Canada

    Joshua Power & Julian Meng

  2. University of Victoria, Victoria, BC, V8P 5C2, Canada

    Derek Jacoby & Yvonne Coady

  3. National Research Council Canada, Ottawa, ON, K1A 0R6, Canada

    Marc-Antoine Drouin & Guillaume Durand

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  1. Joshua Power
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Correspondence to Joshua Power .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. University of Ontario Institute of Technology, Oshawa, ON, Canada

    Bill Kapralos

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Power, J., Jacoby, D., Drouin, MA., Durand, G., Coady, Y., Meng, J. (2023). Toward Data-Driven Glare Classification and Prediction for Marine Megafauna Survey. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_35

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  • DOI: https://doi.org/10.1007/978-3-031-37731-0_35

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