Remote Sensing Image Acquisition and Analysis using MATLAB

2024-01-15

● Course Description:

This course introduces the fundamentals of remote sensing image acquisition and analysis using MATLAB. Topics covered include:

● Image formation and characteristics.

● Image acquisition systems.

● Image preprocessing.

● Image segmentation.

● Feature extraction.

● Image classification.

● Image restoration.

● Image compression.

● Course Objectives:

Upon completion of this course, students will be able to:

○ Understand the fundamentals of remote sensing

○ Apply Image processing techniques on remote sensing data using MATLAB.

○ Communicate the results of remote sensing analysis to a variety of audiences

● Prerequisites:

○ Basic programming skills

○ Calculus

○ Linear algebra

● Textbooks:

○ Fundamentals of Remote Sensing, 4th Edition by J. A. Richards and J. R. Jia.

○ Digital Image Processing using MATLAB, 3rd Edition by R. C. Gonzalez, R. E. Woods, and S. L. Eddins.

● Course Outline:

○ Module 1:  Introduction to remote sensing

○ Module 2: Image formation and characteristics

○ Module 3:  Image acquisition systems

○ Module 4: Image preprocessing

○ Module 5: Image segmentation

○ Module 6: Feature extraction

○ Module 7: Image classification

○ Module 8: Image restoration

○ Module 9: Image compression

● Course Delivery Method:

○ Lectures: Interactive lectures delivered by experienced remote sensing professionals and experts through presentations, videos, and demonstrations.

○ Homework Assignments: There will be three homework assignments throughout the course. The homework assignments will be graded on correctness and completeness.

○ Project: The project will involve applying machine learning to a remote sensing problem. Students will work in groups of two or three to complete the project. The project will be graded on creativity, originality, and technical quality.

○ Q&A Sessions: Regular question and answer sessions to clarify doubts and address participants' queries related to the course content and practical exercises.

● Course Duration:

4 weeks (can be adjusted as per requirements)

● Note:

The course content and duration can be customized according to the specific requirements and level of expertise of the target audience.