COURSE SYLLABUS - Fall 2004

Geography 352 - Image Analysis (Digital Remote Sensing)

This course is a basic introduction to many aspects of image analysis as they apply to remote sensing. The emphasis will be on digital remote sensing, i.e. analyzing data in digital form using computer software.  Remote sensing has steadily grown in importance since the early 1970s and continues to expand as sensing technology improves, as imagery becomes cheaper, as coverage becomes more widespread and as good software for processing the data become readily available.

This course has a practical focus; as well as learning the scientific and technical issues, you will be expected to be able to put these into practice and become proficient in the handling and processing of remote sensing imagery.  Consequently, laboratory work will play a major component in this course, and will be reflected in the overall assessment. Lectures and labs will cover the major stages of importing, registering, processing and evaluating digital imagery and will introduce students to various types of remote-sensing data sources and applications.

Taking a broader perspective, we will also examine the roles that remote sensing can and cannot play, the use of remote sensing in many practical geographic analysis and modeling tasks, the integration of remote sensing with GIS and the likely future of remote sensing as imaging technology continues to improve.

Keeping in Touch

Professor: Mark Gahegan, office: 312b Walker Bldg. office hours: 10-11, Tuesday and Thursday and by appointment. email: mng1@psu.edu, phone: 865‑2612.  My regular office hours are the times when I should always be there.  Please don’t hesitate to phone or email if you wish to make an appointment outside of my posted office hours. Or you can just call in, but I do reserve the right to be busy!

Teaching Assistant: Chaoqing Yu, office: 225 Walker Bldg. office hours: 1:10-2:10pm on Tuesday and Thursday  in 225 Walker, or by appointment. email: cxy164@psu.edu, phone:  865-7432.

Teaching internship:  Bradley Farster, office: 225 Walker Bldg. office hours: 5:45-7:00pm on Tuesday; email:  bjf198@psu.edu

Text

The text we will be using is: Introductory Digital Image Processing: A Remote Sensing Perspective by John R. Jensen (Prentice Hall); you can use either the 2nd or the brand new 3rd edition—it is up to you. Another good alternative is Remote Sensing Digital Image Analysis by Richards, J. A. and Jia, X. (1998), 3rd edition (Springer Verlag). Either book is acceptable, and will serve you well.  When readings are set prior to class, you are expected to comply.


Topics                                                                                                                                      ( Jensen  |  Richards)

1. Introduction and motivation
            Why use remote sensing? The nature of images. Some motivating examples. ( Ch 1       |       Ch 1)

2. Review of remote sensing technology (Ch 2       |         Ch 1)
            Characteristics of digital image data.
            Different types of sensors and their uses.

3. Interpretation of digital image data    (Ch 3     |         Ch 3)
            Approaches to interpretation: quantitative analysis and photo-interpretation.
            Human centered approaches: photo-interpretation & photogrammetry.
            Machine centered approaches: supervised and unsupervised classification.

4. Radiometric enhancement techniques (contrast)    (Ch 5-7      |      Ch 4)
            Controlling image brightness, Color tables, color lookup.
            Histogram equalization, Contrast matching.

5. Filters and image enhancement    (Ch 7     |        Ch 5)
            Neighborhoods and templates.
            Image smoothing and noise removal.
            Edge detection, line detection and shape detection.

6. Clustering and unsupervised classifiers     (Ch 8     |      Ch  9)

            Approaches to clustering.

            The k-means algorithm.

            Operation and methodology for unsupervised classification.

7. Supervised classification techniques and methodologies    (  Ch 8    |       Ch 8,11)
            Principles of maximum likelihood classification.
            Operation and methodology for maximum likelihood.
            Training data: characteristics and preparation.

8. Transformations on image data        ( Ch 7     |       Ch 6 )
            Band ratio techniques and image indexes (e.g. NDVI).
            Principle components and dimensionality reduction.
            Tasseled Cap transformation.

9. Advanced topics      (these are examples… we might cover different ones    ( Ch 9      |      Ch 12, 13)
            Remote sensing of the cryosphere, remote sensing and the climate
            Working with hyperspectral imagery (very high spectral resolution platforms).
            Change detection, Lidar, Microwave remote sensing.

Laboratory Operations

Computer labs and practical work for the term projects will normally be carried out in the Computer lab in Walker 229, (PCs running Windows NT). At least one of the labs will be in the form of a local field trip.


Evaluation Criteria

            Three tests (one will be a ‘take home test’) 15% each             45%

            Individual Assignment                                                            15%,

            Lab. Work                                                                            20%,

            Group Term Project                                                               20%.

  •  Lab work will be regularly assessed by mini quizzes and one-on-one questioning.  Details will be explained to you during the first lab.
  • The group term project will involve producing your own classified landcover map of a local region.  You will be expected to carry out all processing activities, including data preparation and fieldwork.   For this exercise you will be assigned to a team.



     

EMS Academic Integrity Statement

From the college website…  “Academic integrity is the pursuit of scholarly activity in an open, honest and responsible manner. Academic integrity is a basic guiding principle for all academic activity in the College, and all members of the College are expected to act in accordance with this principle. Consistent with this expectation, all students should act with personal integrity, respect other students' dignity, rights and property, and help create and maintain an environment in which all can succeed through the fruits of their efforts.”

 

 

Academic integrity includes a commitment not to engage in or tolerate acts of falsification, misrepresentation, or deception. Such acts of dishonesty violate the fundamental ethical principles of the EMS community and compromise the worth of work completed by others.

Students who engage in dishonest academic activity will be penalized according to the EMS college guidelines.  Full details of dishonest activities and their penalties are available at http://www.ems.psu.edu/admin/tables.html.  Students are cautioned that the penalties outlined on this website will be in force for the duration of this course.