CSc I6732: Information Theory
with Applications to Satellite Image Processing

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, rate distortion and hypothesis testing.

The subject of information, and various ways in which it can be represented, is of course closely linked to the study of data compression. As the semester progresses, we will increasingly stress the compression aspects of the subject, and the course will conclude with a detailed study and analysis of data representing observations of the Earth from space. Understanding and predicting changes in Earth's environment is increasingly dependent on the management of vast quantities of data. For example, the rich data streams produced by next-generation instruments planned for upcoming environmental satellites will need to be compressed and protected from the inevitable presence of error introduced during transmission.
This is a subject of considerable practical importance, and one which is currently being very actively pursued at the Cooperative Remote Sensing Science and Technology Center (CREST) of CCNY. It is expected that as the course progresses, students who show signs of demonstrated interest and promise in this area may, at the instructor's discretion, be eligible for funding, which is available for the support of work in this area.