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Remote Sensing Image Acquisition, Analysis and Applications

Approx. 23 hours to complete

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Course Summary

This course teaches the principles of remote sensing and its applications in various fields such as agriculture, forestry, and environmental science.

Key Learning Points

Learn how to interpret remote sensing data from different sensors and platforms
Understand the various applications of remote sensing in different fields
Acquire hands-on experience in processing and analyzing remote sensing data

Learning Outcomes

Ability to interpret and analyze remote sensing data
Hands-on experience in using remote sensing software
Understanding of the various applications of remote sensing in different fields

Prerequisites or good to have knowledge before taking this course

Basic knowledge of GIS
Familiarity with image processing software

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on projects

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Geospatial Intelligence & Remote Sensing
Satellite Remote Sensing: Earth Observation
Introduction to GIS

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Description

Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles.

This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning. It assumes no prior knowledge of remote sensing but develops the material to a depth comparable to a senior undergraduate course in remote sensing and image analysis. That requires the use of the mathematics of vector and matrix algebra, and statistics. It is recognised that not all participants will have that background so summaries and hand worked examples are included to illustrate all important material. The course material is extensively illustrated by examples and commentary on the how the technology is applied in practice. It will prepare participants to use the material in their own disciplines and to undertake more detailed study in remote sensing and related topics.

Outline

Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz
Course Introduction
Welcome to Module 1
Module 1 Lecture 1 What is remote sensing
Module 1 Lecture 2 The atmosphere
Module 1 Lecture 3 What platforms are used for imaging the earth's surface?
Module 1 Lecture 4 How do we record images of the earth's surface?
Course instructions
Instructor biography
Text of slide audio files for Module 1
End-of-lecture quiz answers
Week 1 Quiz

Week 2 Lectures and Quiz
Module 1 Lecture 5 What are we trying to measure?
Module 1 Lecture 6 Distortions in recorded images
Module 1 Lecture 7 Geometric distortion in recorded images
Module 1 Lecture 8 Correcting geometric distortion
Week 2 Quiz

Week 3 Lectures and Quiz
Module 1 Lecture 9 Correcting geometric distortion using mapping functions and control points
Module 1 Lecture 10 Resampling
Module 1 Lecture 11 An image registration example
Module 1 Lecture 12 How can images be interpreted and used?
Module 1 Lecture 13 Enhancing image contrast
Week 3 Quiz

Week 4 Lectures and Quiz
Module 1 Lecture 14 An introduction to classification (quantitative analysis)
Module 1 Lecture 15 Classification: some more detail
Module 1 Lecture 16 Correlation and covariance
Module 1 Lecture 17 The principal components transform
Week 4 Quiz

Week 5 Lectures and Quiz, Module 1 Test
Module 1 Lecture 18 The principal components transform: worked example
Module 1 Lecture 19 The principal components transform: a real example
Module 1 Lecture 20 Applications of the principal components transform
Instructions for test and data to be used when answering questions
Week 5 Quiz
Module 1 Test questions and your answers

Module 2 Introduction, Week 6 lectures and Quiz
Welcome to Module 2
Module 2 Lecture 1: Fundamentals of image analysis and machine learning
Module 2 Lecture 2: The maximum likelihood classifier
Module 2 Lecture 3: The maximum likelihood classifier—discriminant function and example
Module 2 Lecture 4: The minimum distance classifier, background material
Text of slide audio file for Module 2
End of lecture quiz solutions
Week 6 Quiz

Week 7 Lectures and Quiz
Module 2 Lecture 5: Training a linear classifier
Module 2 Lecture 6: The support vector machine—training
Module 2 Lecture 7: The support vector machine—the classification step and overlapping data
Module 2 Lecture 8: The support vector machine—non-linear data
Module 2 Lecture 9: The support vector machine—multiple classes and the classification step
Module 2 Lecture 10: The support vector machine—an example
Week 7 Quiz

Week 8 Lectures and Quiz
Module 2 Lecture 11: The neural network as a classifier
Module 2 Lecture 12: Training the neural network
Module 2 Lecture 13: Neural network examples
Week 8 Quiz

Week 9 Lectures and Quiz
Module 2 Lecture 14: Deep learning and the convolutional neural network, part 1
Module 2 Lecture 15: Deep learning and the convolutional neural network, part 2
Module 2 Lecture 16: Deep learning and the convolutional neural network, part 3
Module 2 Lecture 17: CNN examples in remote sensing
Module 2 Lecture 18: Comparing the classsifiers
Week 9 Quiz

Week 10 Lectures and Quiz, Module 2 Test
Module 2 Lecture 19: Unsupervised classification and clustering
Module 2 Lecture 20: Examples of k means clustering
Module 2 Lecture 21: Other clustering methods
Module 2 Lecture 22: Clustering "big data"
Reading: Instructions for test and data to be used when answering questions
Week 10 Quiz
Module 2 Test questions and your answers

Module 3 Introduction, Week 11 Lectures and Quiz
Welcome to Module 3
Module 3 Lecture 1: Feature reduction
Module 3 Lecture 2: Exploiting the structure of the covariance matrix
Module 3 Lecture 3: Feature reduction by transformation
Module 3 Lecture 4: Separability measures
Module 3 Lecture 5: Distribution-free separability measures
Text of slide audio file for Module 3
End of lecture quiz solutions
Week 11 Quiz

Week 12 Lectures and Quiz
Module 3 Lecture 6: Assessing classifier performance and map errors
Module 3 Lecture 7: Classifier performance and map accuracy
Module 3 Lecture 8: Choosing testing pixels for assessing map accuracy
Module 3 Lecture 9: Classification methodologies
Module 3 Lecture 10: Other interpretation methods
Week 12 Quiz

Week 13 Lectures and Quiz
Module 3 Lecture 11: Fundamentals of radar imaging
Module 3 lecture 12: Summary of SAR and its practical implications
Module 3 Lecture 13: The scattereing coefficient
Module 3 Lecture 14: Speckle and an introduction to scattering mechanisms
Week 13 Quiz

Week 14 Lectures and Quiz
Module 3 Lecture 15: Radar scattering from the earth's surface
Module 3 Lecture 16: Sub-surface imaging and volume scattering
Module 3 Lecture 17: Scattering from hard targets
Module 3 Lecture 18: The cardinal effect, Bragg scattering and scattering from the sea
Week 14 Quiz

Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion
Module 3 Lecture 19: Geometric distortions in radar imagery
Module 3 Lecture 20: Geometric distortions in radar imagery, cont.
Module 3 Lecture 21: Radar interferometry
Module 3 Lecture 22: Radar interferometry for detecting change
Module 3 Lecture 23: Some other considerations in radar remote sensing
Module 3 Lecture 24: The course in review
Course Closing Comments
Instructions for test and data to be used when answering questions
Week 15 Quiz
Module 3 Test questions and your answers

Summary of User Reviews

Discover the fascinating world of remote sensing with this highly rated course on Coursera. Users praise the course for its engaging content, strong instructor, and practical applications. One key aspect that many users thought was good is the real-world examples and exercises that help reinforce key concepts.

Pros from User Reviews

Engaging and practical course content
Well-structured and easy to follow
Expert and experienced instructor
Real-world examples and exercises
Useful and relevant information

Cons from User Reviews

Some users found the course too basic
Limited opportunities for interaction with other students
Not enough hands-on experience with remote sensing tools
Some technical issues with the online platform
The course may not be suitable for advanced learners

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