MWF 12:30-1:30

Prof. Christopher Ruf

This course is intended to introduce the student to remote sensing methods.  Physical processes are introduced that relate unknown geophysical parameters to measurements.  The mathematical expression for these processes is referred to as the í¢â‚¬Å“forward modelí¢â‚¬ .  A number of methods of applied mathematics, signal processing and estimation theory are used in remote sensing to invert the forward model and solve for the geophysical parameters.  These methods will be developed in this course. The emphasis of the course is on developing and implementing signal and image processing algorithms and on characterizing their performance.  Some of the topics that are covered include the sharpening of blurred images by deconvolution, the extraction of atmospheric constituent profiles from integrated radiative transfer measurements, the optimal design of spectrometers by maximizing the information content of the measurements, Bayesian and optimal estimation methods, and linear and non-linear methods of regression analysis.  This course should be of interest to students who work with remotely sensed data, to gain a better understanding of the relationship between the raw radiance measurements and the estimated geophysical parameters.  It is well suited to students with an interest in the application of formal signal theory concepts to problems of current interest in remote sensing.  These methods are also useful for students interested in the design of remote sensing instruments.