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SURV413 Resource Mapping and Image Processing

Digital image pre-processing and processing, including advanced processing techniques. Field data collection, image classification, and image enhancement. Students will produce a resource map and critically evaluate its accuracy based upon literature searches and field checks.

The increasing number of imaging sensors and platforms offers new exciting capabilities to observe and characterise our environment. Dealing with imagery requires a specific set of skills that are addressed in this paper. This paper also provides an opportunity to gain practical experience with various types of imagery and to complete a comprehensive research project involving the use of images.

Paper title Resource Mapping and Image Processing
Paper code SURV413
Subject Surveying
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,176.90
International Tuition Fees (NZD) $4,410.00

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Prerequisite
SURV 309 or SURV 318
Restriction
SURV 513, SURV 424
Schedule C
Science
Eligibility
Suitable for graduates and professionals of all disciplines with initial knowledge of remote sensing technologies and interested in getting advanced knowledge and experience in image processing techniques

This paper is offered at the 500-level specifically to support postgraduate students aiming at using remote sensing in their research, as well as professionals seeking to gain new skills in geospatial sciences.
Contact
pascal.sirguey@otago.ac.nz
Teaching staff
Convenor and Lecturer: Dr Pascal Sirguey
Paper Structure
Generally based in the context of remote sensing, this paper is aimed at providing an extended knowledge of image processing techniques used for the mapping of earth resources. It includes classic methods of pre-processing (eg gap-filling, calibration, geometric and radiometric correction), image enhancement (eg radiometric, spatial, multispectral enhancement, principal component analysis) and image classification (unsupervised and supervised), as well as advanced processing techniques (eg multispectral image fusion, spectral unmixing, fuzzy and object-orientated classification). The methodology for fieldwork, sampling, ground-truthing and accuracy assessment of the final resource maps is addressed to provide students with sufficient knowledge towards the completion of deliverables with a high-quality standard.

Image enhancement and manipulation techniques will be applied and analysed by the student to produce a resource map and critically evaluate its accuracy based upon a literature review and personal experiences.
Teaching Arrangements
The theoretical content of this paper is addressed over two hours of lectures weekly.

Practical experience is gained during 11 practical sessions in a well-equipped computer laboratory, whereby students will be asked to carry out a research-led project that relies on imagery and image processing techniques. This is a major component of the paper.
Textbooks
Recommended: Richards and Jia (2006). Remote sensing digital image analysis. An introduction. 4th Edition, Springer-Verlag, 363pp. (Electronic edition available online from the Dunedin Campus; older editions are available at the library)
Graduate Attributes Emphasised
Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy, Information literacy, Research, Self-motivation, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will
  • Develop an extended understanding of image processing techniques
  • Gain the capacity to design an appropriate image processing protocol according to a specific problematic
  • Produce a thematic map from imagery
  • Be able to evaluate critically the accuracy of mapping outputs
  • Be able to deliver the results in a professional manner

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Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Monday 12:00-12:50 28-34, 36-41
Tuesday 13:00-13:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend
P1 Monday 14:00-16:50 28-34, 36-41

Digital image pre-processing and processing, including advanced processing techniques. Field data collection, image classification, and image enhancement. Students will produce a resource map and critically evaluate its accuracy based upon literature searches and field checks.

The increasing number of imaging sensors and platforms offers new exciting capabilities to observe and characterise our environment. Dealing with imagery requires a specific set of skills that are addressed in this paper. This paper also provides an opportunity to gain practical experience with various types of imagery and to complete a comprehensive research project involving the use of images.

Paper title Resource Mapping and Image Processing
Paper code SURV413
Subject Surveying
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
SURV 309 or SURV 318
Restriction
SURV 513, SURV 424
Schedule C
Science
Eligibility
Suitable for graduates and professionals of all disciplines with initial knowledge of remote sensing technologies and interested in getting advanced knowledge and experience in image processing techniques

This paper is offered at the 500-level specifically to support postgraduate students aiming at using remote sensing in their research, as well as professionals seeking to gain new skills in geospatial sciences.
Contact
pascal.sirguey@otago.ac.nz
Teaching staff
Convenor and Lecturer: Dr Pascal Sirguey
Paper Structure
Generally based in the context of remote sensing, this paper is aimed at providing an extended knowledge of image processing techniques used for the mapping of earth resources. It includes classic methods of pre-processing (e.g. gap-filling, calibration, geometric and radiometric correction), image enhancement (eg radiometric, spatial, multispectral enhancement, principal component analysis) and image classification (unsupervised and supervised), as well as advanced processing techniques (eg multispectral image fusion, spectral unmixing, fuzzy and object-orientated classification). The methodology for fieldwork, sampling, ground-truthing and accuracy assessment of the final resource maps is addressed to provide students with sufficient knowledge towards the completion of deliverables with a high-quality standard.

Image enhancement and manipulation techniques will be applied and analysed by the student to produce a resource map and critically evaluate its accuracy based upon a literature review and personal experiences.
Teaching Arrangements
The theoretical content of this paper is addressed over two hours of lectures weekly.

Practical experience is gained during 11 practical sessions in a well-equipped computer laboratory, whereby students will be asked to carry out a research-led project that relies on imagery and image processing techniques. This is a major component of the paper.
Textbooks
Recommended: Richards and Jia (2006). Remote sensing digital image analysis. An introduction. 4th Edition, Springer-Verlag, 363pp. (Electronic edition available online from the Dunedin Campus; older editions are available at the library)
Graduate Attributes Emphasised
Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy, Information literacy, Research, Self-motivation, Teamwork.
View more information about Otago's graduate attributes.
Learning Outcomes
Students who successfully complete the paper will
  • Develop an extended understanding of image processing techniques
  • Gain the capacity to design an appropriate image processing protocol according to a specific problematic
  • Produce a thematic map from imagery
  • Be able to evaluate critically the accuracy of mapping outputs
  • Be able to deliver the results in a professional manner

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Blackboard

Lecture

Stream Days Times Weeks
Attend
L1 Monday 12:00-12:50 28-34, 36-41
Tuesday 13:00-13:50 28-34, 36-41

Practical

Stream Days Times Weeks
Attend
P1 Monday 14:00-16:50 28-34, 36-41