Courses & Units
Advanced Earth Observation KGG543
Introduction
This unit will provide you with high-level theoretical knowledge and practical skills in image analysis techniques, such as geometric image correction, image filters, texture measures, vegetation indices, LiDAR point cloud filtering, classification algorithms, object-based image analysis, change detection, and accuracy assessment. The theory is illustrated with a range of real-world applications using optical, hyperspectral, and RADAR imagery, and LiDAR data. Computer practicals and an independent project promote practical remote sensing skills using the latest image processing software. Throughout the unit you will explore remote sensing research topics in the scientific literature that will prepare you for a research thesis.
Summary
Unit name | Advanced Earth Observation |
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Unit code | KGG543 |
Credit points | 12.5 |
College/School | College of Sciences and Engineering School of Geography, Planning, and Spatial Sciences |
Discipline | Geography, Planning, and Spatial Sciences |
Coordinator | Doctor Steve Harwin |
Delivered By | University of Tasmania |
Level | Postgraduate |
Availability
Location | Study period | Attendance options | Available to | ||
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Hobart | Semester 1 | On-Campus | International | Domestic |
Key
- On-campus
- Off-Campus
- International students
- Domestic students
Note
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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.
Key Dates
Study Period | Start date | Census date | WW date | End date |
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Semester 1 | 20/2/2023 | 21/3/2023 | 10/4/2023 | 28/5/2023 |
* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (refer to How do I withdraw from a unit? for more information).
Unit census dates currently displaying for 2023 are indicative and subject to change. Finalised census dates for 2023 will be available from the 1st October 2022. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
Learning Outcomes
- Evaluate image analysis techniques to enhance the interpretation and classification of remote sensing datasets
- Develop remote sensing analysis workflows to solve environmental and social problems that require spatial solutions
- Operate remote sensing software to implement image analysis workflows that achieve client-driven outcomes
Fee Information
Field of Education | Commencing Student Contribution 1,3 | Grandfathered Student Contribution 1,3 | Approved Pathway Course Student Contribution 2,3 | Domestic Full Fee 4 |
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031103 | $1,037.00 | $1,037.00 | not applicable | $2,938.00 |
1 Please refer to more information on student contribution amounts.
2 Please refer to more information on eligibility and Approved Pathway courses.
3 Please refer to more information on eligibility for HECS-HELP.
4 Please refer to more information on eligibility for FEE-HELP.
If you have any questions in relation to the fees, please contact UConnect or more information is available on StudyAssist.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
Requisites
Prerequisites
KGG542Teaching
Assessment | Assignment1: prac 1 - 4 (20%)|Assignment2: prac 4 - 7 (20%)|Assignment4: multiple choice and case study (25%)|Assignment3: project (35%) |
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Timetable | View the lecture timetable | View the full unit timetable |
Textbooks
Required |
Required readings will be listed in the unit outline prior to the start of classes. |
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Recommended | Jensen, J.R., 2014. Remote Sensing of the Environment: An Earth Resource Perspective, 2nd edition. Prentice Hall. https://www.booktopia.com.au/remote-sensing-of-the-environment-john-r-jensen/book/9781292021706.html | Links | Booktopia textbook finder |
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