GELb 311: Remote Sensing-II- Image Processing and Analysis

Course IDCourse NameInstructorRoom NumberTime
GELb 311 Remote Sensing-II- Image Processing and AnalysisMd. Faruk Hossain206 (GIS & RS Lab 1 ) & 232 (GIS & RS Lab 2)2:30-4:50 P.M. (Sunday)

Introduction to Digital Image and Image Processing System
1.1 Introduction to digital image processing- Concept of digital image, steps in DIP
1.2 Image processing systems –hardware and software considerations
1.3 Digitization of photographic image, converting digital image to visual form image
1.4 Digital image data formats, Image data storage and retrieval
Labs:
    1. Introduction with DIP Software
    2. Image Data Format and Exchange/Import

Image Restoration and Rectification
2.1 Radiometric correction of remotely sensed data
2.2 Geometric correction of remotely sensed data
2.3 Image registration – definition principle and procedure
2.4 Basic statistical concepts in DIP and use of probability methods in DIP
Labs:
    3. Image Registration
    4. Radiometric and Geometric Correction of Image
    5. Basic Statistics of Image

Image Enhancement and Analysis
3.1 Image enhancement Techniques – an overview
3.2 Contrast Enhancement – Linear and non-linear, Histogram equalization and Density slicing
3.3 Spatial filtering and Edge enhancement
3.4 Multi image manipulation – addition, subtraction and Band rationing
Labs:
    6. Image Enhancement (various types of)
    7. Spatial Filtering (different types of)
    8. Band ratio and analysis and comparison

Principal Component Analysis and Image Transformation
4.1 Principal Component Analysis
4.2 Enhancement by using colors – advantages, Types of color enhancements
4.3 BGR – coding and generation of FCC’s
4.4 Image transformation – Intensity Hue Saturation (HIS)
Labs:
    9. PCA Analysis
    10. BGR and FCC image generation and analysis
    11. Image transformation

Pattern Recognition and Image Classification
5.1 Pattern recognition and image classification, Unsupervised classification – advantage, disadvantage and limitations
5.2 Supervised classification – training site selection, Classifiers used in supervised classification – Minimum distance to mean, Parallelepiped, maximum likelihood
5.3 Classification accuracy assessment
5.4 Hyper-spectral image analysis
Labs:
    12. Image Classification (Supervised and Unsupervised)
    13. Classification Accuracy Assessment
    14. Exercise on Image Classification (based on research articles)

Suggested Readings:

  • Drury, S.A., 1987: Image Interpretation in Geology. Allen and Unwin
  • Gibson, P.J. 2000: Digital Image Processing. Rutledge Publication
  • Gupta, R.P., 1990: Remote Sensing Geology. Springer Verlag.
  • Joseph George, 2003: Fundamentals of remote sensing. Universities Press
  • Lillesand, T.M., and Kieffer, R.M., 1987: Remote Sensing and Image Interpretation, John Wiley.
  • Nag P. and Kudrat M. 1998: Digital Remote Sensing. Concept Publication
  • Pratt.W.K. 2004: Digital Image processing. John Wiley
  • Sabbins, F.F., 1985: Remote sensing Principles and interpretation. W.H.Freeman and company
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