John Hopkins University - Remote Sensing Image Acquisition, Analysis and Applications 

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