Because of the enormous increase in the availability and utilization of remotely sensed data related to the Earth, the School of Computational Sciences (SCS) has developed a new graduate certificate in Remote Sensing and Earth Image Processing (RSEIP) to meet the needs of prospective students, area employers, and society at large. This Certificate is administered by the Earth Systems and Geoinformation Sciences (ESGS) Program within the School of Computational Sciences. The RSEIP certificate requires students to complete a set of 15-credit hours of SCS graduate courses. Ideal candidates for this certificate are those who have a background in Earth and environmental sciences, and are either currently working in or planning to enter into the fields of remote sensing, Earth observing, or image processing.

We believe that the RSEIP certificate will prove very attractive to students who are interested in advancing their career goals, but who may not have adequate time available to undertake a graduate degree program. The 15-credit certificate is based upon the set of core courses currently supporting the Earth Observing/Remote Sensing area of concentration within the Computational Sciences and Informatics (CSI) Ph.D. program, along with a set of elective courses. Students completing the RSEIP certificate will receive the most up-to-date advanced remote sensing and Earth observing education available in the region. Completion of the certificate will enhance the careers of those students who are already working in this area, and can also serve as a useful intermediate step towards later enrollment in the Earth Systems Science (ESS) M.S. degree program, and/or the CSI doctoral program.

1. Applicants to the RSEIP graduate certificate program should hold a B.A. or B.S. degree in a discipline related to the science and applications of remote sensing from an accredited university, with a minimum GPA of 3.000.

2. Applicants should have some prior education or training in remote sensing and/or image processing. Students with a background in one of the physical science areas (physics, chemistry, atmospheric science, hydrology, or geology), geography, or environmental science will be particularly well suited to undertake this certificate program. Applicants should have undergraduate backgrounds that include courses in differential and integral calculus, and they should possess working knowledge of a computer programming language.

3. Applicants should submit the followings materials:

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The Remote Sensing and Earth Image Processing (RSEIP) Certificate requires a total of 15 credit hours, or five 3-credit courses. Students are required to take four core courses, plus a fifth course selected from the list of electives indicated below.

Core Courses: (all are required)

740 Hyperspectral Imaging Systems (3:3:0). Prerequisites: CSI 660 or equivalent, or permission of instructor. This course provides the requisite materials to understand hyperspectral imaging technology and its many civilian and military applications. The emphasis is on the scientific principles involved and the application of the technology to real-world imaging systems. Topics covered include hyperspectral concepts and system tradeoffs; data collection systems; calibration techniques; data processing techniques and software; classification methods; and case studies. The data processing techniques covered include N-dimensional spaces; scatterplots; spectral angle mapping; spectral mixture analysis; spectral matching; mixture tuned matched filtering; and other techniques. Ground, airborne, and spaceborne hyperspectral remote sensing systems are discussed.

EOS 753 Observations of the Earth and its Climate (3:3:0). Prerequisites: CSI 660 or an introductory remote sensing course; environmental science, space science, physics, or chemistry undergraduate background; or permission of instructor. Provides the requisite material to understand techniques of remote sensing and other observational methods as applicable to Earth science and global change. Surveys methodologies and their applications, including a systematic study of how each part of the electromagnetic spectrum is used to gather data about Earth. Describes limitations imposed by satellite engineering, sensor limitations on data gathering, and a survey of data reduction specific to remote sensing applications. Also covers current research issues, including examples pertaining to the atmosphere, land masses, and oceans. Includes discussions of current efforts by agencies such as NASA and NOAA to provide integrated data gathering and dissemination systems.

EOS 754 Earth Observing/Remote Sensing Data and Data Systems (3:3:0). Prerequisites: EOS 753 or permission of instructor. Covers how to access and apply Earth observations/remote sensing data for Earth system science research and applications. Major topics are data formats, analysis and visualization tools, advanced data analysis methods, and data applications. The course also covers combining innovative information technology techniques and Earth science data to set up online data centers for web users to be able to access data through the web.

EOS 756 Physical Principles of Remote Sensing (3:3:0). Prerequisites: EOS 753 or permission of instructor. This course emphasizes the fundamental physical and mathematical principles of remote sensing. It also provides an overview of the current Earth Observation System (EOS), as well as the National Polar-Orbiting Operational Environmental Satellite Systems (NPOESS), and the NPOESS Preparatory Project (NPP) missions.

EOS 757 Techniques and Algorithms in Earth Observing and Remote Sensing (3:3:0). Prerequisites: EOS 753 or permission of instructor. Covers retrieval, analysis, and application of geophysical parameters derived from remotely sensed data for Earth system research and applications. Includes theory of visible/infrared and microwave remote sensing, heritage sensors, sensor calibration, retrieval algorithms, validation, and error estimates.

EOS 758 Digital Processing of Remote Sensing Imagery (3:3:0). Prerequisites: EOS 753, GEOG 580, or permission of instructor. An intermediate-advanced level course focusing on digital processing of Earth images, with significant coverage on the topics of hyperspectral images; mathematical and algorithmic foundations; analysis procedures; and computational implementations. Programming projects will be emphasized.

EOS 760 Remote Sensing Applications (3:3:0). Prerequisites: EOS 753 or GEOG 580. This course focuses on the applications of remote sensing in various important areas of Earth systems science, such as analysis of the surface radiation budget, land cover, inland/coastal waterways, and soil moisture. Algorithms/techniques and examples are discussed in detail.

EOS 840 Hyperspectral Imaging Applications (3:3:0). Prerequisites: CSI 660 or equivalent, or permission of instructor. Introduces advanced hyperspectral imaging and multi-sensor concepts with emphasis on real-world civilian and military applications. Topics covered include advanced hyperspectral concepts, multi-system tradeoffs, data collection and processing systems, imaging radar systems, laser systems, calibration techniques, data fusion, quantitative remote sensing techniques, data compression techniques, case studies, and U.S national policy. Applications and case studies will include environmental, homeland security, medical, military, disaster mitigation, agricultural, and transportation.

Copyright School of Computational Sciences, George Mason University, Fairfax, VA, USA
Last modified: December 28, 2005

Please send questions or comments to Dr. Peter A. Becker at  pbecker@gmu.edu

Graphic design: Janejira Kalsmith
Programming: Guido Cervone an d Liviu Panait