Course: Remote Sensing Image Acquisition, Analysis and Applications
Modules:
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?
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
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
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
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
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
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
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
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
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"
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
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
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
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
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