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Course Information Undergraduate prospectus

Introduction to Remote Sensing

Course summary

Course code: GEOL1008
Level: 7
Credits: 15
School: Engineering and Science
Department: Pharm, Chemical and Envi Science
Course Coordinator(s): Meredith Williams

Specification

Aims

The aim of this course is to introduce the students to the concepts and principles behind remote sensing and the characteristics of remotely sensed data, their collection, processing, and display. This course also aims to introduce basic image processing techniques through `hands-on' experience.

This course explores the theories and techniques behind Earth Observation (EO) and remote sensing data acquisition. The course also provides a theoretical introduction to a wide variety of remote sensing platforms and sensors. Students receive practical experience in dealing with a varied range of remotely sensed data using ERDAS Imagine, one of the most common commercial image processing packages. This course develops the basic concepts of image processing, which are dealt with in greater detail in the course GEOL 1009 `Advanced Image Processing'.

Learning outcomes

On completing this course successfully you will be able to:
1. Critically reflect on the physics of electromagnetic spectrum and their importance in Earth Observation, specifically in the use of Visible, Infrared and Microwave remote sensing;
2. Critically evaluate the roles and limitations of satellite, aerial & terrestrial remotely sensed imagery in a variety of contexts;
3. Critically reflect on the hardware and software requirements for quantitative interpretation of remotely-sensed imagery;
4. Critically analyse airborne photogrammetric data sets;
5. Resolve basic remote sensing problems using commercial software packages such as ERDAS Imagine.

Indicative content

The nature of the electromagnetic spectrum, the laws of radiation and physical principles of remote sensing.
Interaction of radiation with the atmosphere and Earth surface.
Introduction to remote sensing platforms and sensors; data capture and data types.
Basic image pre-processing: noise & artefact removal, radiometric correction, atmospheric correction, geometric correction.
Simple visual and numerical enhancement techniques for digital imagery; such as contrast stretching, convolution filtering, and hard classifiers.
The theory of filter construction in the spatial and spectral domains; to include smoothing filters, edge enhancement filters and directional filters, Fourier filtering, and the problem of artefacts.
Image histogram manipulation through linear and non-linear transforms.
Visible to thermal infrared wavelength imaging.
The basic principles of microwave remote sensing and RADAR.
Photo-interpretation of aerial photographs, and analogue photogrammetric techniques.

Teaching and learning activity

A combination of lecture based teaching and lab based learning methods are employed to provide students with the necessary knowledge and opportunities to achieve the learning outcomes.
Lectures - 50% of contact
Practical Work - 50% of contact


Learning Time (1 credit = 10 hours)

Scheduled contact hours:
lectures 24
seminars 2
other practical sessions 18
tutorials
examinations 3
other scheduled time
Guided independent study

Independent coursework 30
Independent laboratory work
other non-scheduled time 73
Placements (including work placement and year abroad) 0
Total hours (should add to credits * 10) 150

Assessment

Coursework 1 - 20% weighting. Pass mark 50%. 1000 words. Conference style poster presentation and short talk introducing the poster. Learning outcomes 1 and 2.
Coursework 2 - 30% weighting. Pass mark 50%. All eight of the compulsory 2 hours computing practicals will have a short answer sheet, for submission within one week of the practical. These will be assessed and a combined score assigned at the end of the course. Assesses learning outcomes 3,4.5. Theory exam - 50% weighting. Pass mark 50%. 3 hour exam. Students answer 3 essay questions out of 6, related to the topics covered in the lectures and practicals. Assesses learning outcomes 1,2,3 and 4.