This unit builds on the theory and skills of KGG103 Remote Sensing: Introduction and focuses on advanced aspects of remotely sensed image analysis. These additional remote sensing analysis skills are highly valued by employers in the spatial industry. The unit will provide you with practical skills in image analysis techniques, such as geometric and atmospheric image correction, image filters, texture measures, image enhancements and transformations, 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 in the latest image processing software. The unit is likely to be of interest to students in geography, environmental studies, earth sciences, plant science, zoology, agricultural science, computing and information systems, archaeology, and engineering who want to enhance their remote sensing knowledge and professional skills.
|Unit name||Remote Sensing: Image Analysis|
|College/School||College of Sciences and Engineering
School of Geography, Planning, and Spatial Sciences
|Discipline||Geography, Planning, and Spatial Sciences|
|Coordinator||Doctor Stephen Harwin|
|Available as an elective?||Yes|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
- International students
- Domestic students
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|Study Period||Start date||Census date||WW date||End date|
* 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).
- Explain image analysis techniques to inform the interpretation and enhancement of remote sensing datasets.
- Apply analysis techniques on remote sensing datasets to solve environmental and social problems that require spatial solutions
- Operate remote sensing software to produce enhanced spatial information from basic datasets
|Field of Education||Commencing Student Contribution 1||Grandfathered Student Contribution 1||Approved Pathway Course Student Contribution 2||Domestic Full Fee|
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.
Please note: international students should refer to What is an indicative Fee? to get an indicative course cost.
2 x 1-hr lectures weekly, 13 x 3-hr lab classes
|Assessment||Assignment 4: multiple choice and case study (25%)|Assignment 3: Project (35%)|Assignment1: pracs 1 - 4 (20%)|Assignment2: pracs 5 - 7 (20%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
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• 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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