GEO5050 - Digital Image Processing

Course Description

Course Outline

Learning objectives and topics covered:

• Major satellite and terrestrial sensors
• Image geometry; rectification, enhancement
• Image Transforms – PCA and Fourier Transform, wavelet and derivatives
• Image Filtering
• Image classification – supervised, unsupervised, and hybrid classifiers
• Image segmentation and target detection 
• Image quality and accuracy assessment
• Data fusion
• Thematic feature extraction

Learner Outcomes

• Students will be able to identify sources of various satellite and sensor data to complete the lab assignment or class projects, which will be evaluated based on their lab submissions
• Students will be able to learn identify problems in the images (distortion, noise, or geometric error) and apply image transform techniques to correct the imagery. The learning outcomes of these will be evaluated using three labs focused on identifying image quality evaluation, assessment and correction. Students will submit a report with the results of these labs and responding a set of questions based on the labs
• Students will be able to conduct supervised and unsupervised classification and segmentation of various image data which is demonstrated by lab assignments and class project
• Students will be able to apply information and decision level fusion of imagery data which will be measured by a final project
• Pre-class reading is evaluated by pre-class quizzes distributed during the semester. Students are provided reading materials, and submit their responses to a set of questions developed based on the reading materials before each class 

Duration