Digital Image Analysis & Advanced Remote Sensing 
A GOG 585 - A GOG 485

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Required Text & Other Materials Lab Assignments Lab Exercises Exams Research Project Course Requirement Values Course Schedule	Instructor: Floyd M. Henderson Office: AS 211 Office Hours: announced each semester Phone: 442-3912 Webpage: http://www.albany.edu/~fmh06
	Required Text & Other Materials: Text: J. Jensen, Introduction to Digital Image Analysis, 2nd Edition, Prentice-Hall,1995 Other materials: USGS Topographic Maps and Computing disks as announced. Lab Assignments: There will be six (6) assigned labs to introduce basic image processing principles. Dates and time allowed for each lab will be announced in class.

Exams:

There will be a Mid-Term Exam perhaps given in one or two parts during the semester. Times will be announced in class.

Research Project:

Each student will be required to complete a research project using software and hardware available in the GISRS Computing Lab or another topic approved by the instructor. The subject MUST BE approved by the instructor. The project idea must be submitted and approved by an announced date). Unapproved delays beyond this time will result in a penalty of one (1) percentage point of the project per day! It is the student's responsibility to contact and meet with the instructor and obtain approval by this date. 

Research Paper projects MUST conform to the following format:
	Introduction/Background
	Purpose
	Methodology/Study Area
	Analysis
	Conclusions and Observations
	References (Citations and Bibliography)
	(Figures and Tables as Appropriate)

The study area for the project must be one from the list of TM and other digital image sets provided in class (Albany; Thacher Park; Saratoga Lake; Saratoga Springs; Delmar; Clifton Park-Mohawk River). Selection of any other scene (e.g. from the Web) must be approved by the instructor prior to the announced date. 

Course Requirement Values:

Labs:	20%
Exam(s):	35%
Project:	45%

Course Schedule:

Digital Image Analysis Class Schedule (preliminary schedule)
MSS THEORY AND DATA BASES
	MSS Principles and Review
	LANDSAT, SPOT, and TM systems
	Radar/SAR systems
PREPROCESSING
	Histograms
	Data Formats
	Geometric Corrections
	Skew and Resampling
	Radiometric Corrections
	Noise Correction
	Atmospheric Scattering
IMAGE ENHANCEMENT
	Reduction/Magnification
	Contrast Stretches
	Linear Gaussian
	Area Sinusoidal
	Intensity-Hue-Saturation
	Ratios-Part I
FILTERING
	Spatial Domain
	Low Frequency
	High Frequency
	Frequency Domain
	Fourier Analysis
	Edge Enhancement
	Linear
	Non-linear (first difference)
RATIOING
	Combinations
	Uses
TRANSFORMATIONS
	Vegetation Indices (tasseled cap, et.al.)
	Texture
	Principal Component Analysis
	Canonical Analysis
IMAGE CLASSIFICATION
	Density Slicing
	Training Sets
	Supervised Classification
		Minimum Distance	Mahalanobis
		Parallelepiped	Baysian
		Maximum Likelihood	Others
	Unsupervised Classification
		Clustering
		Information Classes
		Spectral Classes
	Hybrid Classification
ACCURACY ASSESSMENT
	Sampling
FIELD VERIFICATION as appropriate

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