GI3402 Digital Image Processing Syllabus:

GI3402 Digital Image Processing Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

 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

Definition – Image Representation – Steps in DIP-– Components – Elements of Visual Perception – Image Formation – Image Sampling and Quantization- Image acquisition, storage and retrieval –– Relationships between pixels – Color image fundamentals – RGB, HSI models- data products – satellite data formats – Digital Image Processing Systems – Hardware and software design consideration.

UNIT II PREPROCESSING

Image Characteristics – Histograms – Scattergrams –Initial statistics – Univariate and multivariate statistics-Initial image display- Ideal display, types, Sensor models – spatial, spectral, radiometric, temporal – IFOV, GIFOV& GSI – geometry and Radiometry – Sources of Image degradation and Correction procedures – Atmospheric, Radiometric, Geometric Corrections- Image Geometry Restoration-Interpolation methods and resampling techniques.

UNIT III IMAGE ENHANCEMENT

Image characteristics- point, local and regional operation – contrast, spatial feature and multi-image manipulation techniques – level slicing, contrast stretching, spatial filtering, edge detections – Fourier transform-FFT, DFT – Band ratio – Principal Component Analysis (PCA) – Scale-space transformmulti-image fusion.

UNIT IV IMAGE CLASSIFICATION

Pattern recognition concepts – Bayes approach – spectral Signature and training sets – Separability test – Supervised Classification – stages – Minimum distance to mean, Parallelepiped, MLC – Unsupervised classifiers – ISODATA, K-means-Support Vector Machine – sub-pixel classifier– Error matrix -Accuracy assessment – accuracy metrics: Kappa statistics, ERGAS, RMS.

UNIT V ADVANCED CLASSIFIERS

Texture based classification -Segmentation (Spatial, Spectral)-regions Fuzzy set classification – Object based classifiers – Deep Learning – Artificial Neural nets: Hebbian leaning – Adaline, Madaline, BPN – hybrid classifiers – Neuro – Fuzzy models- Expert system – Knowledge based systems,

TOTAL:45 PERIODS
COURSE OUTCOMES:

 On completion of the course, the student is expected to
CO1 To understand various components and characteristics of image processing systems
CO2 To familiarize the concepts of image geometry and radiometry corrections
CO3 To acquire knowledge about different types of image enhancement techniques used for satellite image processing
CO4 To gain knowledge about Image classification and accuracy assessment of various classifiers
CO5 To acquaint with various advanced classification techniques available for feature extraction

TEXTBOOKS:

1. John,R.Jensen, Introductory Digital Image Processing, Prentice Hall, NewJersey, 2021 Fourth edition.
2. Robert,A.Schowengergt, Techniques for Image Processing and classification in Remote Sensing,1983.

REFERENCES:

1. Robert, G. Reeves,-Manual of Remote Sensing Vol.I &II- American Society of Photogrammetry ,Falls ,Church, USA,1983.
2. John.A Richards, Remote sensing digital Image Analysis – An Introduction Springer-Verlag, Fifth Edition, 2014.
3. Digital Image Processing by Rafael C. Gonzalez, Richard Eugene Woods – Pearson/Prentice Hall,Fourth edition, 2022.
4. Fundamentals of Digital Image Processing by Annadurai Pearson Education (2007)