NERC GW4+ DTP PhD studentship: Remote sensing and DEEP learning for early warning of WATER hazards (DeepWater) Ref: 2632

About the award

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus six Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Met Office, the Natural History Museum and Plymouth Marine Laboratory.  The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science.

Applications are now being accepted for 3 fully-funded studentships on projects that cover the breadth of earth and environmental sciences commencing September 2017. Studentships will provide funding for stipend and fees (currently £14,553 and £4,195 per annum respectively) a Research and Training Support Grant of £11,000 and a training budget of £4,000 for 42 months (3.5 years).  All projects will be CASE projects with input into project development from key end-user communities.

Location:

Department of Computer Science, Streatham Campus, University of Exeter

Academic Supervisors:

Dr Chunbo Lou, University of Exeter (Main Supervisor)

Dr Stefan Simms, Plymouth Marine Laboratory

Dr Samantha Lavendar, Pixalytics Ltd (CASE partner)

Prof. Geyong Min, University of Exeter

Prof. Edward Keedwell, University of Exeter

Prof. Dragan Savic, University of Exeter

Project Description:

Current satellite sensors allow global monitoring of water resources at an unprecedented optical and spatial resolution, paving the way for operational monitoring of potential hazards from space. Of particular interest is the potential value of satellite observations during and following episodic events such as heavy wind and rain, which may lead to harmful algal blooms, sewage overflow and poor visibility.

Project Aims and Methods

The overarching objectives of this project are to develop image segmentation and object-based satellite image processing techniques for cases where water quality issues are evident. A successful monitoring solution would provide hazard warning information to the affected companies and end users.

The first such case concerns mapping river plumes and their ‘sphere of influence’. Tracing river plumes from their source to the furthest extent, visible as an optical and/or radar signature, directly informs commercial (e.g. aquaculture) and recreational (e.g. diving, fishing, surfing) use of potential risks in the event of strong storm runoff.

The second case focuses on mapping of dynamic features such as potentially harmful algal and cyanobacterial blooms, and relatively stable features such as shallow areas (bottom visibility), and floating vegetation, in coastal and inland water systems. Object oriented mapping would classify these optically dominant structures and create a novel approach to spatial binning on satellite imagery.

The project will seek to tackle the challenge by exploiting the new generation of high resolution satellite imagery (Sentinel-1 (radar), Sentinel-2 (optical), and Landsat optical missions) and numerous advanced computing techniques which have not yet been applied in remote sensing of water bodies [1], e.g. deep learning, sub-pixel level endmember extraction methods, local parallelisation and distributed processing techniques etc.

Candidate

The project would suit a student with a first degree in Computer Science and a desire to develop a range of skills such as remote sensing, machine learning and computer vision techniques. A candidate with the background of remote sensing and satellite image processing is also encouraged to apply.

Case Award description

This is a CASE award. The student will spend a minimum of 3 months at the CASE Partner, Pixalytics(Satellite imagery expert). Dr Lavender is the Managing Director of Pixalytics Ltd, a Trustee of SAHFOS, Chairman of the British Association of Remote Sensing Companies and Honorary Reader of Geomatics at Plymouth University. She has 20+ years research experience in the fields of this project.

Training

The funded student will benefit from collaboration and training opportunities through the EU H2020 EOMORES project and by working closely with the CASE partner. The student will attend a training course on Computer Vision and Machine Learning in the first year, usually hosted by the University of Exeter and IEEE Computer Vision Society. Dr Simis will guide them through his training course on Aquatic Optics, previously taught at the University of Helsinki, and relevant chapters of the book ‘Light and Photosynthesis in Aquatic Ecosystems’ by JTO Kirk (1994).  The student will be notified of summer school opportunities organized by the International Ocean Colour Coordinating Group (IOCCG), and be encouraged to join the Remote Sensing and Photogrammetric Society (RSPSoc) Wavelength group that supports remote sensing students and early career professionals. 

References / Background reading list

  • Xingrui Yu, Xiaomin Wu, Chunbo Luo & Peng Ren, Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework, GIScience & Remote Sensing, 2017.
  • H. Zhang, C. Luo et al., "Systematic infrared image quality improvement using deep learning based techniques", SPIE Security + Defence 2016.
  • B. Pan, Z. Shi, Z. An, Z. Jiang and Y. Ma, "A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for GOCI Data," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 2, pp. 437-449, Feb. 2017.              

Entry requirements:

Studentships are open to UK resident students and applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK. Applicants with a Lower Second Class degree will be considered if they also have a Master’s degree. Applicants with a minimum Upper Second Class degree and significant relevant non-academic experience are encouraged to apply

If English is not your first language you will need to have achieved at least 6.5 in IELTS and no less than 6.0 in any section by the start of the project.  Alternative tests may be acceptable, see http://www.exeter.ac.uk/postgraduate/apply/english/.

Summary

Application deadline:25th June 2017
Value:£14,553
Duration of award:per year
Contact: CLES PGR Admin phone 01392 725150/723706exeter-nerc-gw4+@exeter.ac.uk

How to apply

Click here to apply

In the application process you will be asked to upload several documents.  Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”. 

  • CV
  • Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
  • Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
  • If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English

You will be asked to name 2 referees as part of the application process however we will not contact these people until the shortlisting stage. Your referees should not be from the prospective supervisory team.

The closing date for applications is midnight on 25th June 2017.  Interviews will need to be held on the University of Exeter Streatham Campus before July 7th 2017.

If you have any general enquiries about the application process please email exeter-nerc-gw4+@exeter.ac.uk or phone +44 (0)1392 725150/723706.  Project-specific queries should be directed to the main supervisor c.luo@exeter.ac.uk