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

DateTopic
Jan-07Introduction
Jan-10Landsat 8, 9; sentinel
Jan-11Intro to PCI Catalyst, Sentinel
Jan-14Landsat-like sensors
Jan-17Principal Component Analysis /hyper
Jan-18Principal Component Analysis / Tassel cap 
Jan-21High-Resolution sensors
Jan-24DEMs 
Jan-25DEMs/Topographic co-registration
Jan-28High Res Presentations – class
Jan-31 Low/med res: MODIS 
Feb-01LiDAR  
Feb-04Planetary Remote Sensing
Feb-07MODIS – Presentations – class
Feb-08RPAS Data 
Feb-11SAR- amplitude
Feb-14INSAR
Feb-15SAR
Feb-18Midterm 1
Feb-21Family Day
Feb-22Reading Break
Feb-25Reading Break
Feb-28OOC-segmentation
Mar-01OOC – segmentation
Mar-04Machine Learning
Mar-07Cube / RPAS data
Mar-08Machine Learning
Mar-11Large Datasets / Area
Mar-14Time series
Mar-15Google Earth Engine / Planetary Engine (Alex)
Mar-18Applications: environmental monitoring
Mar-21Applications: emergency management
Mar-22Project 
Mar-25Course review / summary
Mar-28Lecture exam2
Mar-29Project
Apr-01RPAS demo
Apr-04Class project demos
Apr-05Project (20%)