JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objective

to understand the advance image enhancement, analysis and management

techniques

Course Outcome

At the end of the course the student will able to classify the processed remote

sensing data, apply the advanced processing methods for deriving the useful information and Classify, and analyze Hyper spectral data

UNIT - I

Image Resolution: Correction and calibrations- Noise Reduction – Global, Local and Periodic, radiometric calibrations, Senor Calibrations, Atmospheric Correction, Topographic Correction.

UNIT - II

Advance Enhancement: Advanced Enhancement Techniques. PCA, Tasseled Cap Transformation, MNF Transformation, Independent Component Analysis. Color Transformation – RGB to IHS, Natural Color Transformation. Data Fusion Techniques: IHS, Multiplicative, Brovey and Wavelet Based Transformation.

UNIT - III

Image Compression Techniques: Data compression: Methods- lossless & Lossy –Techniques- DFT, wavelet, JPEG, Cosine transformations.

UNIT - IV

Classifications: Parametric & Non Parametric- Training sets Statistics, Minimum distance to mean, Maximum Likelihood, ANN, Weight Vector, Support Vector Machine, Decision Tree Approach, Concepts of hierarchical decision tree for Multi Temporal data, GLCM & Texture based classifiers. Sub pixel classifiers: Fuzzy Classification. K Mean, ISO Data, Fuzzy K Means, Fuzzy ML Classifiers, Expert and Artificial Intelligence Based Systems, Mathematical Morphology.

UNIT - V

Hyper Spectral Image & Change Detection Analysis: Data Visualization, Dimensional Reduction, Feature Extraction – Characteristic, Pixel Purity Index, Hyper Spectral Mapping Methods: - SAM, Spectral Feature Filtering, Spectral Unmixing. Change Detection: Nature of Change Detection, Introduction to Change Detection algorithms. Post Classification- Comparison, Ratioing, Vegetative Indices, Change Vector Analysis.

TEXT BOOKS:

• 1. John R. Jenson, “Introductory Digital Image Processing”, Prentice Hall Series, 1996.
• 2. John A. Richards, Springer-Verlag, “Remote Sensing Digital Image Analysis” 1999.

REFERENCES:

nd

• 1. Rafael C. Gonzalez, “Digital Image Processing (2 Edition)”, Prentice Hall, 2002. nd
• 2. Remote sensing models and methods for Image processing – Schowengerdt 2 edition
• 3. Anil K. Jain “Fundamentals of Digital Image Processing” Prentice Hall Publications, USA.
• 4. David L. Verbyla “Satellite Remote sensing of Natural Resource Management”, Lewis publishers, Florida
• 5. Image Analysis, Classification and change Detection in Remote Sensing – Mortan J.Century, Taylor and Francis, 2007. M. Tech – I Year – II Sem. (GIST)