Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Course Instructions
Course instructions
Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Welcome to the Course: Remote Sensing Data Acquisition, Analysis and Applications
Course Introduction
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Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Biography of the Instructor
Instructor biography
Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Text of Slide Audios for Module 1
Text of slide audio files for Module 1
Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Solutions to End of Lecture Self Checking Quiz Questions for Module 1
End-of-lecture quiz answers
Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Welcome to Module 1: Acquiring images and understanding how they can be analysed
Welcome to Module 1
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Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz-Week 1 Lectures and Quiz
Module 1 Lecture 1 What is remote sensing
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Module 1 Lecture 2 The atmosphere
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Module 1 Lecture 3 What platforms are used for imaging the earth's surface?
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Module 1 Lecture 4 How do we record images of the earth's surface?
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Week 2 Lectures and Quiz-Week 2 Lectures
Module 1 Lecture 5 What are we trying to measure?
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Module 1 Lecture 6 Distortions in recorded images
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Module 1 Lecture 7 Geometric distortion in recorded images
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Module 1 Lecture 8 Correcting geometric distortion
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Week 3 Lectures and Quiz-Week 3 Lectures
Module 1 Lecture 9 Correcting geometric distortion using mapping functions and control points
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Module 1 Lecture 10 Resampling
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Module 1 Lecture 11 An image registration example
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Module 1 Lecture 12 How can images be interpreted and used?
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Module 1 Lecture 13 Enhancing image contrast
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Week 4 Lectures and Quiz-Week 4 Lectures
Module 1 Lecture 14 An introduction to classification (quantitative analysis)
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Module 1 Lecture 15 Classification: some more detail
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Module 1 Lecture 16 Correlation and covariance
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Module 1 Lecture 17 The principal components transform
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Week 5 Lectures and Quiz, Module 1 Test-Week 5 Lectures
Module 1 Lecture 18 The principal components transform: worked example
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Module 1 Lecture 19 The principal components transform: a real example
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Module 1 Lecture 20 Applications of the principal components transform
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Week 5 Lectures and Quiz, Module 1 Test-Module 1 Test
Instructions for test and data to be used when answering questions
Module 2 Introduction, Week 6 lectures and Quiz-Text of Slide Audios for Module 2
Text of slide audio file for Module 2
Module 2 Introduction, Week 6 lectures and Quiz-Solutions to End of Lecture Self-Checking Quiz Questions for Module 2
End of lecture quiz solutions
Module 2 Introduction, Week 6 lectures and Quiz-Welcome to Module 2: Computer-based interpretation – fundamentals of machine learning
Welcome to Module 2
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Module 2 Introduction, Week 6 lectures and Quiz-Week 6 lectures
Module 2 Lecture 1: Fundamentals of image analysis and machine learning
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Module 2 Lecture 2: The maximum likelihood classifier
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Module 2 Lecture 3: The maximum likelihood classifier—discriminant function and example
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Module 2 Lecture 4: The minimum distance classifier, background material
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Week 7 Lectures and Quiz-Week 7 Lectures
Module 2 Lecture 5: Training a linear classifier
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Module 2 Lecture 6: The support vector machine—training
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Module 2 Lecture 7: The support vector machine—the classification step and overlapping data
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Module 2 Lecture 8: The support vector machine—non-linear data
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Module 2 Lecture 9: The support vector machine—multiple classes and the classification step
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Module 2 Lecture 10: The support vector machine—an example
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Week 8 Lectures and Quiz-Week 8 Lectures
Module 2 Lecture 11: The neural network as a classifier
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Module 2 Lecture 12: Training the neural network
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Module 2 Lecture 13: Neural network examples
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Week 9 Lectures and Quiz-Week 9 Lectures
Module 2 Lecture 14: Deep learning and the convolutional neural network, part 1
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Module 2 Lecture 15: Deep learning and the convolutional neural network, part 2
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Module 2 Lecture 16: Deep learning and the convolutional neural network, part 3
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Module 2 Lecture 17: CNN examples in remote sensing
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Module 2 Lecture 18: Comparing the classsifiers
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Week 10 Lectures and Quiz, Module 2 Test-Week 10 Lectures
Module 2 Lecture 19: Unsupervised classification and clustering
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Module 2 Lecture 20: Examples of k means clustering
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Module 2 Lecture 21: Other clustering methods
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Module 2 Lecture 22: Clustering "big data"
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Week 10 Lectures and Quiz, Module 2 Test-Module 2 Test
Reading: Instructions for test and data to be used when answering questions
Module 3 Introduction, Week 11 Lectures and Quiz-Text of Slide Audios for Module 3
Text of slide audio file for Module 3
Module 3 Introduction, Week 11 Lectures and Quiz-Solutions to End of Lecture Self-Checking Quiz Questions for Module 3
End of lecture quiz solutions
Module 3 Introduction, Week 11 Lectures and Quiz-Welcome to Module 3: Computer-based interpretation in practice, and remote sensing with imaging radar
Welcome to Module 3
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Module 3 Introduction, Week 11 Lectures and Quiz-Week 11 Lectures
Module 3 Lecture 1: Feature reduction
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Module 3 Lecture 2: Exploiting the structure of the covariance matrix
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Module 3 Lecture 3: Feature reduction by transformation
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Module 3 Lecture 4: Separability measures
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Module 3 Lecture 5: Distribution-free separability measures
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Week 12 Lectures and Quiz-Week 12 Lectures
Module 3 Lecture 6: Assessing classifier performance and map errors
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Module 3 Lecture 7: Classifier performance and map accuracy
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Module 3 Lecture 8: Choosing testing pixels for assessing map accuracy
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Module 3 Lecture 9: Classification methodologies
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Module 3 Lecture 10: Other interpretation methods
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Week 13 Lectures and Quiz-Week 13 Lectures
Module 3 Lecture 11: Fundamentals of radar imaging
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Module 3 lecture 12: Summary of SAR and its practical implications
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Module 3 Lecture 13: The scattereing coefficient
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Module 3 Lecture 14: Speckle and an introduction to scattering mechanisms
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Week 14 Lectures and Quiz-Week 14 Lectures
Module 3 Lecture 15: Radar scattering from the earth's surface
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Module 3 Lecture 16: Sub-surface imaging and volume scattering
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Module 3 Lecture 17: Scattering from hard targets
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Module 3 Lecture 18: The cardinal effect, Bragg scattering and scattering from the sea
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Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion-Untitled Lesson
Module 3 Lecture 19: Geometric distortions in radar imagery
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Module 3 Lecture 20: Geometric distortions in radar imagery, cont.
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Module 3 Lecture 21: Radar interferometry
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Module 3 Lecture 22: Radar interferometry for detecting change
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Module 3 Lecture 23: Some other considerations in radar remote sensing
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Module 3 Lecture 24: The course in review
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Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion-Module 3 Test
Instructions for test and data to be used when answering questions
Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion-Course Conclusion
Course Closing Comments
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