Semester Offering: August

This course aims at providing students with principles of Remote Sensing (RS) technology, which is the tool to obtain information on the earth from deci-meter level to km level locally and globally. Basic image processing techniques and skill to analyze Remote Sensing image will be taught as well. Application examples of remote sensing technologies to various fields will be introduced to encourage students to use remote sensing in their research.


Overview of RS Application. Interaction between electromagnetic wave and targets. Satellite System and Sensors. Introduction to RS Image Processing. Image Enhancement. Geometric Correction. RS Image Classification.




I.       Introduction
1.      Overview and Concepts of RS technology
2.      Various Satellites and Sensors

II.     Overview of RS Application
1.      Disaster management; Volcano, Flood, Forest Fire
2.      Agriculture applications
3.      Landuse / landcover monitoring
4.      Fishery and marine application
5.      Coastal Zone Management
6.      Urban monitoring
7.      Remote Sensing and Model Calibration

III.    Interaction between electromagnetic wave and targets
1.         Definition of Radiometry
2.         Refraction , absorption , diffusion , emission with Radiometric terms and units
3.         Spectral Responses at various targets
4.         Radiometric distortion and correction
5.         Atmospheric correction

IV.    Satellite System and Sensors
1.         High-Resolution optical satellites : LANDSAT, SPOT, ASTER, ALOS, IRS, IKONOS, GeoEye-1, Quick Bird, WorldView-1, EO-1
2.         Moderate-Low Resolution satellites: NOAA, SPOT Vegetation, MODIS.
3.         Synthetic Aperture Radar (SAR )

V.     Introduction to RS Image Processing
1.         Pixel
2.         Sampling & Quantization
3.         File Formats

VI.    Image Enhancement
1.           Image Statistics
2.           Contrast Enhancement
3.           Color and Color Composite
4.           Math Operation
5.           Principal Component Analysis

VII. Geometric Correction
1.         Internal and external distortions
2.         Map Projection
3.         Coordinate Transformation Formulas
4.         Resampling and Interpolation

VIII. RS Image Classification
1.         Classification of multi-spectral data
2.         Unsupervised Classification
3.         Supervised Classification
4.         Post-Classification


will be conducted so that student will be able to manipulate and analyze remote sensing images.


Lecture notes distributed on-line


John A. Richards, Xiuping Jia:
         Remote Sensing Digital Image Analysis : An Introduction, Springer, 4th Edition, 2005

R.C. Gonzales, R. E. Woods:
         Digital Image Processing, Prentice Hall, 3rd Edition, 2007

R. A. Schowengerdt:
         Techniques for Image Processing and Classification in Remote Sensing, Academic Press, 1983

Space Agencies and Satellite Operation Companies’ on-line Manuals 




Final grades will be computed according to the following percentage distribution:

Mid-semester Examination 30%
Final Examination 40%
Assignments/Lab Assignments/Mini Project 30%.

Examination will be closed book.