ENEE 759D: Data Structures and Algorithms for Remote Sensing Data Processing

Course Goals:

Remotely sensed data is the primary source of information to study the earth's environment at regional and global scales. Raw satellite data have to be processed and integrated into a database system to produce multitemporal data sets that can be used for various environmental studies. The main goal of the course is to introduce basic algorithms and related data structures for processing remtotely sensed imagery with a particular emphasis on spatial data structures.

Course Prerequisite(s):

ENEE 446 and some programming experience with C.

Topics Prerequisite(s):

Basic understanding of computer architecture and programming in high level programming language such as C. The student is also expected to have had at least some preliminary exposure to elementary data structures and algorithms.

Textbook(s)

None.

Reference(s):

1. Hanan Samet, The Design and Analysis of Spatial Data Structures, Addison-Wesley, Reading, Mass., 1990.

2. Paul Mather, Computer Processing of Remotely Sensed Images, John Wiley, 1987.

3. John Richards, Remote Sensing Digital Image Analysis, An Introduction, Springer-Verlag, 1995.

4. John Schott, Remote Sensing: The Image Chain Approach, Oxford University Press, 1997.

Core Topics:

• A Quick Introduction to Remote Sensing
• Basic Data Processing Steps
• Fundamental Spatial Data Structures: Quadtrees, k-d Trees, and Variants.
• Bucket Methods for Multidimensional Point Data: Grid Based Methods and Hierarchical Access Methods.
• Image Processing Techniques: Image Registration, Enhancement, Filtering, and Classification.

Optional Topics:

• Space Filling Curves
• Spatial Access Methods

Course Structure:

Grading Method:

Last Updated:

Oct. 22, 1998