Multispectral imaging is becoming an increasingly important tool for monitoring the earth and its environment from space borne and airborne platforms. Multispectral imaging data consists of visible and IR measurements from a scene across space and spectrum. In this project-based course, students are grouped into teams to work on projects of practical importance in satellite image processing, pattern recognition and data compression.
Statistical Data Analysis and Visualization
This is a two semester capstone course. The student is required to complete a significant project in computer science or engineering under the mentorship of a faculty member. In addition to technical material required for successful completion of a specific project, topics include identification of a problem, background research, social, ethical and economic considerations, intellectual property and patents and proposal writing, including methods of analysis and theoretical model-ing. A detailed project proposal is formulated in the first semester, and the project is completed in the second semester. Each student is required to write an in-depth report, and to make an oral presentation to the faculty.
Data Fusion Module in Python with Applications to Remote Sensing
On April 20, 2010, an explosion aboard the Deepwater Horizon drilling rig in the Gulf of Mexico caused one of the worst oil spill disasters in U.S. history. Satellites tracked this oil spill for months. In fact, this is a disaster in which satellite observations (and remote sensing in general) played a primary and pivotal role in monitoring the spill and its consequences for the environment. Two NASA satellites were capturing images of the oil spill in the Gulf of Mexico (a short video that reveals a space-based view of the burning oil rig and, later, the resulting spread of the oil spill is available at here and NASA deployed several of its airborne and satellite research sensors to collect an unprecedented amount of remotely sensed data over the Gulf of Mexico region.