Thursdays (labs), 1:15-4:15pm (AKST): WRRB 004 (remote sensing lab)
Large parts of the class will follow a textbook published by Woodhouse (2006). This textbook is listed in the following list of reading materials as required (R) reading. In addition to this book, other supplemental (S) books are listed in the table, which provide good summaries of the material covered in the class.
Original research articles from refereed journals will be assigned as reading to cover specific recent developments in microwave remote sensing and related disciplines. The articles will be made available through the class website or similar means in due time.
For many of the more involved processing labs of this class, we will be using (where necessary) a cloud-based Virtual SAR Lab. Using such a virtual lab has numerous advantages over physical computer pools. They come pre-installed with all relevant processing tools and spare you the painful trouble of installing each individual package by hand. In a virtual lab, all machines are identical. This ensures similar processing speed at all workstations and identical software behavior for all participants. Finally, we will ensure that all machines are correctly scaled, so that you can complete your assignments in a timely manner without ever running out of space. The Virtual SAR Lab comes preinstalled with all necessary Python tools as well as the software packages SNAP, ISCE, GIAnT, TRAIN, StaMPS, GQIS, and PolSARpro, ready for use during lab-sections and project work. In addition small Matlab programs will be provided and used for selected lab sessions. Most labs can be done from a student’s personal computer. For local students, additional computer access will be provided through the Remote Sensing Computer Lab in WRRR 004 of the UAF campus.
This course will introduce the students to the principles and applications of microwave remote sensing. It includes the sensor technology, platforms and data portals to retrieve data. Principle processing techniques and applications of active and passive microwave remote sensing data will be covered. The laboratory part of the course will provide hands-on experience with special processing techniques and the possibility of using these techniques for a student-defined term project in areas of geology, seismology, volcanology, cryosphere, hydrology, environmental sciences, etc. Advanced processing techniques such as InSAR, or polarimetric SAR are included.
The main goal of this course is to provide students with the background needed to use SAR and InSAR data as a source of geodetic information. The course starts with an introduction to passive and active microwave remote sensing and a discussion of methods of Synthetic Aperture Radar (SAR) processing. It then presents an in-depth description of methods and applications of InSAR, including traditional InSAR methods and advanced InSAR Time Series analysis techniques. A thorough description of the main limitations and error sources of these techniques is included to allow for the retrieval of geodetic information from SAR and InSAR observations. It will be shown that SAR and InSAR data can be used to provide geodetic observations of geodynamic and anthropogenic processes of the earth’s surface and interior with high accuracy and resolution. It will also be shown that SAR data can provide high resolution observations of atmospheric and ionospheric constituents. In conclusion, methods for the integration of geodetic observations from SAR with geophysical models and observations from other sensors will be presented.
Interim short test / quiz: 10%
Homework assignments: 25%
Reading & discussion contribution: 15%
Independent project: 50% (25% presentation, 25% term paper)
Lecture 1 & 2
Introduction; History of Microwave Remote Sensing; some mathematical background
Properties and Propagation of EM Waves
Interaction of Microwaves with the Atmosphere; Interaction with discrete objects
112 – 149
LAB: Analysis and interpretation of microwave images
How to Detect Microwaves; Antennas; Sensor Calibration
PASSIVE MICROWAVE SYSTEMS
ACTIVE MICROWAVE SYSTEMS
Principles of RADAR, Radar Altimetry, Scatterometry
LAB: Ground-based radar demo
Real Aperture and SYNTHETIC APERTURE RADARS (SARs)
SYNTHETIC APERTURE RADAR (Geometric distortions; Speckle; Geocoding of SAR data)
Lecture 10 & Lab 3
SAR Image Acquisition Modes; How to Access SAR Data
LAB: SAR Image Processing and Geocoding
Lab 4 &
LAB: Time-Series Analysis of SAR images with Jupyter Notebooks
POLARIMETRIC SAR (POLSAR)
(Concepts & Applications)
Project Topics Due
InSAR and Phase Unwrapping
DIFFERENTIAL INSAR (d-InSAR)
Term Project Concept Presentations
March 11 – 15: SPRING BREAK (no classes)
LAB: InSAR processing with the SNAP toolbox (Kumamoto Earthquake of 2016)
MID TERM EXAM
Using InSAR in Geophysics
LAB: d-InSAR for Volcano Source Modeling
AI4EO: Artificial Intelligence in Earth Observation
LAB: Change Detection using Convolutional Recurrent Neural Networks (CRNNs)
InSAR Time Series Analysis
(Concepts; PSInSAR Methods)
LAB: Change Detection; Hydrology Mapping from time-series SAR
InSAR Time Series Analysis
(The SBAS Approach; Comparison of SBAS and PSInSAR)
LAB: InSAR Time Series Analysis using the GIAnT Software
Radar Remote Sensing In Polar Regions
LAB: Project Work
Final Project Presentations and Project Writeup
Final Project Presentations and Project Writeup
Homework Tips: Please type or write neatly, keep the solutions in the order assigned and bundle your answers in a single pdf file. Include only relevant computer output in your solutions (a good approach is to cut and paste the relevant output for each problem into an editor such as MS Word or Latex). Also clearly circle or highlight important numbers in the output, and label them with the question number. I also suggest that you to include computer code you may have used to derive your answers, both so that you can refer back to it for future assignments and so that I can identify where a mistake may have occurred. Display numerical answers with a reasonable number of significant figures and with units if the quantity is not dimensionless.
Homework scores are based on clarity of work, logical progression toward the solution, completeness of interpretation and summaries, and whether a correct solution was obtained. I encourage you to discuss homework problems with other students, however the work you turn in must be your own.
Furthermore, as a UAF student, you are subject to the student Code of Conduct. The university assumes that the integrity of each student and of the student body as a whole will be upheld. It is your responsibility to help maintain the integrity of the student community. For additional information, contact the Dean of Student Services or web http://www.alaska.edu/bor/regulation/9r/r09-02.html/. The UAF Honor Code (Student Code of Conduct) defines academic standards expected at the University of Alaska Fairbanks.
A term project, to be completed by the end of the semester, will be aimed at applying skills and expertise acquired during the course to a specific scientific or engineering problem. Students are highly encouraged to define a project of their own (e.g., originating from thesis-related research), but a number of project suggestions (incl. data, samples etc.) will also be offered by the instructor.
The instructor is available by appointment for additional assistance outside session hours. UAF has many student support programs, including the Math Hotline (1-866-UAF-MATH; 1-866-6284) and the Math and Stat Lab in Chapman building (see www.uaf.edu/dms/mathlab/ for hours and details).
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