| School of Engineering and Technology, (SET) | ||
| AT76.13 : Remote Sensing Data Analysis 2(1-3) | ||
| Course objectives: | ||
This course aims to develop the skill on understanding, handling and processing of remote sensing data. The course has specific objectives to: i) train students on using various remote sensing data types / formats, imagery products; ii) carryout image and data preprocessing techniques for handling radiometric and geometric corrections; iii) impart knowledge of principles and methods of multi-resolutions and multi-spectral data fusion, multi-temporal processing and accuracy assessment; iv) develop data processing automation through batch processing.
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| Learning Outcomes: | ||
The students on the completion of this course would be able to:
1. Explain and communicate quantitative remote-sensing principles and integrate different tools for remote sensing data analysis.
2. Perform image corrections and enhancements and generate high-level remote sensing products.
3. Manipulate and process RS data using manual and automated techniques
4. Critically compare different type of remote sensing data products and analysis technique and select the more appropriate to solve a real-world problem.
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| Pre-requisite(s): | ||
AT76.03 Remote Sensing |
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| Course Outline: | ||
I. Remote Sensing Raster Data Formats and Main Processing Platforms
1. Remote Sensing data types and formats
2. Commercial and open source remote sensing data processing options
II. Remote Sensing Imagery: Main Online Archives and Products
1. Multi-source and multi-resolution data products (Landsat, ASTER, MODIS, EO-1, DTMs, Sentinel-2.
2. Sentinel data products and download from the Copernicus Open Access Hub
3. Modis data products and download from different NASA and USGS data repositories
4. Common high resolution data products (Digital Globe, SPOT etc.)
III. Techniques of Radiometric and Geometric Correction
1. Atmospheric effects, TOA reflectance and dark object subtraction (DOS) technique
2. Orthorectification with rigorous camera models, rational function model (RFM) and automatic point measurement (APM) techniques,
IV. RS Image Fusion
1. Basic concepts
2. Pansharpening Techniques
3. Quality assessment
V. Multitemporal Remote Sensing and Accuracy Assessment
1. Multitemporal remote sensing (incl. time series): principles and concepts
2. Post classification comparison
3. Multitemporal accuracy assessment: principles and concepts
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| Laboratory Sessions: | ||
1. ERDAS IMAGINE basic and advanced processing capabilities (incl. raster format management, advanced subset, layer stack and mosaicking; Spatial Modeler for batch processing).
2. Radiometric correction (exoatmospheric TOA reectance, dark object subtraction within ERDAS IMAGINE model maker).
3. Geometric Correction using rational function model (RFM) and automatic point measurement (APM) techniques.
4. Pansharpening of very high resolution multispectral images.
5. ESA Sentinel Toolboxes: visualization, analysis and processing tools for different Sentinel products.
6. Multitemporal RS: bitemporal change detection and accuracy assessment.
7. Multitemporal RS: Time series analysis of Chlorophyll concentration estimation.
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| Learning Resources: | ||
| Textbook: | ||
No designated textbook, but class notes and handouts will be provided. |
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| Reference Books: | ||
John A. Richards:
Remote Sensing Digital Image Analysis - An Introduction (Fifth Edition). Springer-Verlag Berlin Heidelberg, 2013.
R. G. Congalton, K. Green:
Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition, Boca Raton, CRC Press, 2009.
Gustavo Camps – Valls, Lorenzo Bruzzone:
Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons, Ltd, 2009.
J. G. Liu, P.J. Mason:
Image processing and GIS for remote sensing: techniques and applications, Chichester, Chichester, Wiley-Blackwell, 2016.
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| Journals and Magazines: | ||
• Remote Sensing, MDPI
• International Journal of Photogrammetry and Remote Sensing; (ISPRS), Elsevier
• Photogrammetric Engineering and Remote Sensing, ASPRS
• Remote Sensing of Environment,Elsevier
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| Time Distribution and Study Load: | ||
Lecture : 15 Hrs
Laboratory : 20 Hrs
Miniproject : 10 Hrs
Group meeting : 15 Hrs
Self-study : 60 Hrs
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| Teaching and Learning Methods: | ||
1. Lectures and class discussion: Students will received the lecture notes and lecture schedule at the beginning of the course, and requested them to read the lecture notes before coming to the class.
2. Laboratory sessions: The laboratory instruction will be provided to the students. Lab instruction will provide a basic guideline for student to learn and be familiar with the remote sensing software and remote sensing data. Students are requested to understand the algorithm of each operation so that they able to operate with other software. The home assignments and discussion are requested to submit.
3. Mini project: Students (as the group project) are asked to propose a mini project. Students are provided miniproject to show their ability to apply tools of Remote Sensing Analysis in problem solving. Data is provided and proposals are evaluated. They are also evaluated extensively on concept and expertise on the Remote Sensing software.
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| Evaluation Scheme: | ||
The final grade will be based on the following weight distribution: assignments (30), final exam (50%), Mini project (20%).
An “A” would be awarded if a student can elaborate the knowledge learned in class by giving his/her own analysis on real case examples given in this course and from journal articles and including assigned readings. A “B” would be awarded if a student shows an overall understanding of all given topics, a “C” would be given if a student meets below average expectation on both knowledge acquired and analysis. A “D” would be given if a student does not meet basis expectations in understanding and analyzing the topics and issues presented in the course.
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| Instructor(s): | ||
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