ECE 57700 - Engineering Aspects of Remote SensingLecture Hours: 3 Credits: 3
Normally Offered: Spring - even years
ECE 30100 and 30200.
Introduction to the concepts of remote sensing, image data (multispectral, hyperspectral, LIDAR, microwave, SAR etc) generation and analysis. Basic principles of data acquisition and measurement in natural scenes. Fundamentals of multispectral and hyperspectral data analysis for complex scenes. Application of signal/image processing, statistical and computational pattern recognition/classification algorithms to these problems. Spatial image processing methods and algorithms as appropriate to land scene data. Remote sensing applications in Geographic Information Systems (GIS). Practice with analysis of actual aircraft and spacecraft data in a cross-disciplinary environment, utilizing software packages such as MULTISPEC, MATLAB, ERDAS IMAGINE, ENVI and ESRI.
To provide primarily graduate students with an introduction to modern remote sensing techniques. The course is intended to prepare the student to undertake research in remote sensing and related areas, as preparation for post graduation professional activities in remote sensing, or as a means of further broadening one's background in the general field of image generation and processing. For students interested in image processing in general, it provides a look at the combined optimization of image design and image analysis, and an introduction to techniques applicable for scenes of high complexity. It is taught in a cross-disciplinary environment, usually involving students from ECE, and some or all of Civil Engineering, Agronomy, Forestry, Agricultural Engineering, Earth and Atmospheric Science, and other schools.
- Signal Theory Methods in Multispectral Remote Sensing, Landgrebe, D., J. Wiley, 2003, ISBN No. 0-471-42028-X.
- ERDAS IMAGINE 9.0 Field Guide and Tour Guides, 2005.
- Information Processing for Remote Sensing, C. H. Chen, World Scientific, 1999.
- Neurocomputation in Remote Sensing Data Analysis, I. Kanellopoulos, G. G. Wilkinson, J. Austin, Springer-Verlag, 1997.
- Remote Sensing Digital Image Analysis: An Introduction, John A. Richards and Xiuping Jia, Springer-Verlag, 1999, ISBN No. 3-540-64860-7.
- Remote Sensing and Image Interpretation, 4 Edition, T. M. Lillesand, R. W. Kiefer and J. W. Chipman, John Wiley and Sons, 2004, ISBN No. 0471152277.
Learning Outcomes:A student who successfully fulfills the course requirements will have demonstrated:
- a knowledge of a number of important technologies based on remote sensing, image data (multispectral, hyperspectral, LIDAR, microwave, SAR etc) generation and analysis. [None]
- application of signal/image processing, statistical and computational pattern recognition/classification algorithms to remote sensing and GIS problems. [None]
- spatial image processing methods and algorithms as appropriate to land scene data. [None]
- remote sensing applications in Geographic Information Systems (GIS). [None]
- practice with analysis of actual aircraft and spacecraft data in a cross-disciplinary environment, utilizing software packages such as MULTISPEC, MATLAB, ERDAS IMAGINE, ENVI and ESRI. [None]
|1||Introduction A. How information is conveyed in remote sensing data B. The nature of multivariate images C. Image space and feature (measurement) space D. Introduction to pattern recognition|
|3||Statistical Pattern Recognition in Remote Sensing A. Decision-making in the face of uncertainty B. Fundamental steps in pattern recognition C. Supervised and unsupervised classification D. Multitemporal and multitype data analysis|
|3||How Scenes Become Signals Become Images A. Electromagnetic energy interactions with the atmosphere B. Characteristics of natural scene reflectance and emittance C. Signal design as applied to sensor design D. Modern Optical and microwave sensor systems E. Ancillary sources (digital) terrain maps, map digitizing, non-imaging sensors, etc.|
|3||Spatial Image Processing and Analysis A. Registration and rectification B. Texture C. Image segmentation D. Map (GIS) analysis techniques|
|1||Familiarizing with remote sensing and GIS software systems|
|3||Other statistical and computational intelligence algorithms such as neural networks and support vector machines, fourier and wavelet related transform techniques, subband analysis and synthesis, data fusion, Markov random fields, and compression of remote sensing data.|
Engineering Design Content:
Engineering Design Consideration(s):