Advanced Remote Sensing
This project-oriented course focuses on advanced classification procedures incorporating digital elevation data, fuzzy and object-oriented classification, and new millennium data sources including ASTER, RADAR, MODIS, LiDAR and high-resolution scenes. Repeat imagery is used to assess local and global changes in land cover, oceanic, atmospheric and/or cryospheric environments.
In this course, students will use industry-standard software such as PCI Catalyst, ArcGIS Pro, QGIS, R and Python to complete advanced analysis of remotely sensed images.
Schedule
Date | Topic |
Jan-07 | Introduction |
Jan-10 | Landsat 8, 9; sentinel |
Jan-11 | Intro to PCI Catalyst, Sentinel |
Jan-14 | Landsat-like sensors |
Jan-17 | Principal Component Analysis /hyper |
Jan-18 | Principal Component Analysis / Tassel capĀ |
Jan-21 | High-Resolution sensors |
Jan-24 | DEMs |
Jan-25 | DEMs/Topographic co-registration |
Jan-28 | High Res Presentations – class |
Jan-31 | Low/med res: MODIS |
Feb-01 | LiDAR |
Feb-04 | Planetary Remote Sensing |
Feb-07 | MODIS – Presentations – class |
Feb-08 | RPAS Data |
Feb-11 | SAR- amplitude |
Feb-14 | INSAR |
Feb-15 | SAR |
Feb-18 | Midterm 1 |
Feb-21 | Family Day |
Feb-22 | Reading Break |
Feb-25 | Reading Break |
Feb-28 | OOC-segmentation |
Mar-01 | OOC – segmentation |
Mar-04 | Machine Learning |
Mar-07 | Cube / RPAS data |
Mar-08 | Machine Learning |
Mar-11 | Large Datasets / Area |
Mar-14 | Time series |
Mar-15 | Google Earth Engine / Planetary Engine (Alex) |
Mar-18 | Applications: environmental monitoring |
Mar-21 | Applications: emergency management |
Mar-22 | Project |
Mar-25 | Course review / summary |
Mar-28 | Lecture exam2 |
Mar-29 | Project |
Apr-01 | RPAS demo |
Apr-04 | Class project demos |
Apr-05 | Project (20%) |