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: 1051211 (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.