Digital Image Processing



RIT COLLEGE OF SCIENCE

COURSE OUTLINE

SIMG-463

I. COURSE TITLE

Digital Image Processing III - Multispectral Digital Image Processing

II. COURSE CATALOG DESCRIPTION

This course discusses the digital image processing concepts and algorithms used for the analysis of hyperspectral, multispectral and multi-channel data in remote sensing and other application areas. Concepts are covered at the theoretical and implementation level using current, popular commercial software packages and high-level programming languages for examples, homework and programming assignments. The requisite multivariate statistics will be presented as part of this course as an extension of the univariate statistics that the students have previously been exposed to. Topics to be covered will include methods for supervised data classification, clustering algorithms and unsupervised classification, multispectral data transformations, data redundancy reduction techniques, image-to-image rectification, and data fusion for resolution enhancement. (Prerequisites: 1051-211 (or equivalent), 1016-351, 1061-352)

III. OBJECTIVES OF THE COURSE

The objective of this course is to provide the student with basic knowledge and skills to analyze hyperspectral, multispectral and multi-channel data using commercially available tools. The student will use pre-existing algorithmic implementations in commercially-available software packages as well as code their own implementations in a high-level programming language such as IDL, MATLAB, C++ or Java.

IV. COURSE OUTLINE:

Multivariate Statistics

Conditional probability

The normal probability distribution

Univariate case

Multivariate case

Statistical distance measures

Data Types

Multi-channel data

Multispectral data

Hyperspectral data

Supervised Data Classification

Training

Minimum distance to the mean classifiers

Parallelepiped classifiers

Maximum likelihood classifiers

Bayesian assumptions

Linear discriminant functions

Mahalanobis distance

Spectral angle mapper (SAM)

Clustering and Unsupervised Classification

Similarity metrics and clustering criteria

Iterative clustering algorithms (migrating means)

Seeding techniques

K-Means

ISODATA

Merging, splitting and deleting classes

Single pass techniques

Multispectral Data Transformations/Data Redundancy Reduction

Eigenvector transformations

Principal components analysis

Kauth-Thomas (KT) tasseled cap transformation

Minimum Noise Fraction (MNF)

Image-to-Image Rectification

Multiple linear regression

Ground control point (GCP) selection

Mapping polynomials

Analysis of variance and error characterization

Data Fusion

Multispectral resolution enhancement

Using color transformations

Radiometry preserving techniques

V. INSTRUCTIONAL TECHNIQUES

Classroom lectures, assigned reading, homework, and programming exercises

VI. METHODS OF EVALUATION

Homework and programming assignments, midterm exam, final exam

VII. BIBLIOGRAPHY

Richards, J.A and X. Jia, Remote Sensing Digital Image Analysis, An Introduction, 3rd Edition, Springer-Verlag, New York, 1999.

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