CE59700 - Multi and Hyperspectral Remote SensingFall 2016
Days/Time: TTh / TBA
Credit Hours: 3
The student will develop an understanding of the fundamentals of primary sensing technologies and data analysis methodologies for land resources remote sensing, with a focus on multispectral and hyperspectral remote sensing data acquisition, processing, and analysis. The student will develop a working capability of widely used commercial remote sensing software (e.g. ERDAS, ENVI, PCI) and be able to apply these capabilities using commercial software.
This upper division undergraduate/graduate level course is designed to introduce students to the principles of optical remote sensing and to teach methods for analysis and interpretation of remotely sensed data. The emphasis of the course is on the remote observation of soil, vegetation and water resources (together referred to as land resources) by airborne and space-based multispectral sensors. Students will be exposed to the latest developments of the technology of hyperspectral sensing and LIDAR, and will learn how to utilize remotely sensed data to support decision making in science, engineering, and agriculture. The fundamentals of microwave sensing will be provided, as time allows.
The course will include topics on data acquisition technologies, atmospheric correction, radiometric and geometric correction and enhancement, registration, image transformations, segmentation, classification and spectral unmixing.
A basic working knowledge of calculus, linear algebra, and physics.
Syllabus, grades, lecture notes, homework assignments, solutions, quizzes, chat room and a message board.
Weekly homework exercises will be assigned. Laboratory exercises will be provided to support learning of software. Demonstrations will be provided by teaching assistant. The teaching assistant will be available for consulting via Skype or Blackboard. Homework should be submitted via Blackboard.
A semester project is required. The student selects the topic with the approval of the professor.
Two midterm exams during the semester and One final exam.
1. Lillesand, T.M., R.W. Kiefer and J.W. Chipman. 2008. Remote Sensing and Image Interpretation. 5th Edition. John Wiley and Sons. Main text for the course. 2. Richards, J. and X. Jia. 2006. Remote Sensing Image Analysis. 4th Edition. Spring. Recommended text. 3. Jones, H.G. and Vaughan, R.A. 2010. Remote Sensing of Vegetation. Oxford University Press. Reference. 4. Liang, S. 2004. Quantitative Remote Sensing of Land Surfaces. John Wiley and Sons. New York. Reference. 5. Landgrebe, D. Signal Theory Methods in Multispectral Remote Sensing. 2003. John Wiley and Sons. Reference.
ProEd Minimum Requirements. Students will be required to analyze remotely sensed data for class projects. Access to PC's with Leica Geosystems ERDAS IMAGINE and ITT ENVI is required. Students will also be able to purchase student licenses of the software.
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