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Beyond a shadow of doubt: RPA coastal surveying and classification in the real world (FINLAYSONUCMP21ARIES)


School of Computing Sciences

About the Project

Coastal remote sensing uses aerial images to track the distribution of features such as vegetation, birds, and mammals [1]. Current sensing systems suffer from illumination problems, particularly shadows, which degrade the images recorded. Indeed, the same feature can induce a different image response when viewed in or out of shadow. Human vision overcomes this obstacle by interpreting the properties and proximity of objects in a scene, but remote sensing systems lack the brain’s interpretive power and regularly mis-classify features.

In this project, building on research in the computer vision community, we will develop image processing methods to help us to ‘see into shadows’ and thereby to better classify the coastal features found in images. Our overarching aim is to develop a vision system which - by seeing into shadows - will significantly improve the accuracy of surveys of the coastal environment.

This exciting project will see you working with a team of computing and natural scientists at the cutting edge of remote sensing research. You will characterise the capture device (measuring the camera’s spectral response [2]) and the statistics of the physical world that is being surveyed, to understand the spectral properties of the coastal environment. You will undertake modelling of the coastal environment in collaboration with Cefas, using a large annotated imagery dataset, with an emphasis on identifying the same image features seen in and out of shadows. You will repurpose [3] and [4] for algorithm development, incorporating our understanding of the camera and the physical world. A particularly novel aspect is that we will incorporate near-infrared into the algorithm formulation. There is evidence [5] that near-infrared can help distinguish shadowed and non-shadowed areas.

The candidate - who should have a scientific or engineering background - will be involved in all aspects of the project. They will be trained in measurement and calibration at the world-leading UEA Colour Lab and will work directly with Cefas. The developed algorithms will be prototyped in Cefas’ classification framework.

Funding Notes

This project has been shortlisted for funding by the ARIES NERC DTP.

Successful candidates who meet UKRI’s eligibility criteria are awarded a NERC studentship covering fees, stipend (£15,285 p.a., 2020-21) and research funding. International applicants (EU/non-EU) are eligible for fully-funded studentships. Please note ARIES funding does not cover visa costs (including immigration health surcharge) or other additional costs associated with relocation to the UK.

Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.

ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside significant relevant non-academic experience.

For further information, please visit http://www.aries-dtp.ac.uk

For more information on the supervisor for this project, please go here https://people.uea.ac.uk/g_finlayson

The type of programme is a PHD

Start of the project is 1st October 2021

The mode of study is full or part time (visa restrictions may apply)

Studentship length is 3.5 years

Funding Notes

Entry Requirements:
Acceptable first degree in Science/Engineering.

References

1. K. Anderson and K.J. Gaston, “Lightweight unmanned aerial vehicles will revolutionise spatial ecology,” Frontiers in Ecology and the Environment, 2013.
2. G.D. Finlayson, “Rank-Based Spectral Sensitivity Estimation,” J. Optical Society of America A, 2016
3. G.D. Finlayson et al., “Colour Constancy at pixel,” J. Optical Society of America A, 2001
4. W.Madden et al., “Illumination Invariant Imaging: Applications in Robust Vision-based Localisation, Mapping and Classification for Autonomous Vehicles’’, Int. Conf. On Robotics and Automation, 2014.
5. D. Rüfenach et al., “Automatic and Accurate Shadow Detection using Near-Infrared Information,” IEEE Trans. PAMI, 2014.

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