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. The capstone course will last two semesters. 
In the first semester, this course will deal, in increasingly detailed fashion as the semester progresses, with concepts and methods that are involved in appropriately defining and analyzing the information content of various kinds of data. We will introduce and discuss concepts from Shannon's treatment of information theory: the basic notions of entropy, relative entropy, and mutual information, and show how they arise as natural answers to questions of data compression, channel capacity, pattern recognition and classification. We will discuss the Bayesian approach to the classification problem, and pattern recognition systems such as segmentation, classification, feature extraction etc.
The second semester will focus on implementations of the classification toolbox and applications to analysis of the data representing observations of the Earth from space. The classification toolbox covers a wide variety of the algorithms. The objective of the project is to build a well-documented library that implements many of these algorithms and to test their performance on a variety of satellite-based earth science data.

Data

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. We have a representative selection of satellite based earth science data from both polar and geostationary orbiting imagers such as 

 

SEVIRI aboard the ESA/EUMETSAT operated Meteosat Second Generation (MSG) satellites and current GOES imagers operated by NOAA are geostationary imagers. The AVHRR aboard the NOAA Polar Orbiting Environmental Satellites and MODIS aboard the NASA Terra and Aqua satellites have polar orbits.

Link : Capstone-CS-CCNY-2008

 

© 2010, NOAA-CREST CSDIRS, CCNY-Glasslab/Michael Grossberg/Irina Gladkova/Paul Alabi

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