CE3023 Satellite Image Processing Syllabus:

CE3023 Satellite Image Processing Syllabus – Anna University Regulation 2021

COURSE OBJECTIVE

 To make the undergraduate Engineering Students understand the concepts, principles, processing of Satellite data in order to extract useful information from them.

UNIT I FUNDAMENTALS OF IMAGE PROCESSING

Information Systems – Encoding and decoding – acquisition, storage and retrieval –data products – satellite data formats – Digital Image Processing Systems – Hardware and software design consideration Scanner, digitizer – photo write systems.

UNIT II SENSORS MODEL AND PRE PROCESSING

Image Fundamentals – Sensor models – spectral response – Spatial response – IFOV,GIFOV& GSI – Simplified Sensor Models – Sampling & quantization concepts – Image Representation& geometry and Radiometry – Colour concepts – Sources of Image degradation and Correction procedures- Atmospheric, Radiometric, Geometric CorrectionsImage Geometry Restoration- Interpolation methods and resampling techniques.

UNIT III IMAGE ENHANCEMENT

Image Characteristics – Histograms – Scattergrams – Univariate and multi variate statistics enhancement in spatial domain – global, local & colour Transformations – PC analysis, edge detections, merging – filters – convolution – LPF, HPF , HBF, directional box, cascade – Morphological and adaptive filters – Zero crossing filters – scale space transforms – power spectrum – texture analysis – frequency transformations – Fourier, wavelet and curvelet transformations.

UNIT IV IMAGE CLASSIFICATION

Spectral discrimination – pattern recognition concepts – Baye‘s approach – Signature and training sets – Separability test –Supervised Classification – Minimum distance to mean, Parallelepiped, MLC – Unsupervised classifiers – ISODATA,K-means-Support Vector Machine – Segmentation (Spatial, Spectral) – Tree classifiers – Accuracy assessment – Error matrix – Kappa statistics – ERGAS, RMS.

UNIT V ADVANCED CLASSIFIERS

Fuzzy set classification – sub- pixel classifier – hybrid classifiers, Texture based classification –Object based classifiers – Artificial Neural nets – Hebbian leaning – Expert system, types and examples – Knowledge systems.

TOTAL : 45 PERIODS
COURSE OUTCOMES:

 On completion of the course, the student is expected to be able to
CO1 Understand about Remote sensing and Image processing systems
CO2 Acquire knowledge about the source of error in satellite image and also toremove the error from satellite image.
CO3 Select appropriate image Enhancement techniques based on image characteristics
CO4 Classify the satellite image using various method and also evaluate theaccuracy of classification.
CO5 Apply the advanced image classification methods and conduct lifelong researchin the field of image processing.

TEXTBOOKS :

1. John, R. Jensen, Introductory Digital Image Processing, Prentice Hall, New Jersey, 4th Edition, 2015.
2. Robert, A. Schowengergt, Techniques for Image Processing and classification in Remote Sensing, Academic Press, 2012.

REFERENCES:

1. Robert, G. Reeves,- Manual of Remote Sensing Vol. I & II – American Society of Photogrammetry, Falls, Church, USA, 1983.
2. Richards, Remote sensing digital Image Analysis – An Introduction 5th Edition, 2012,Springer -Verlag 1993.
3. Digital Image Processing by Rafael C. Gonzalez,Richard Eugene Woods- Pearson/Prentice Hall,2008
4. Fundamentals of Digital Image Processing by Annadurai Pearson Education (2006)
5. Digital Image Processing: PIKS Scientific Inside by William K. Pratt 4th Edition,Wiley Interscience, 2007