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Remote Sensing Image Acquisition, Analysis and Applications
  • Level Professional
  • المدة 23 ساعات hours
  • الطبع بواسطة UNSW Sydney (The University of New South Wales)
  • Offered by

عن

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.

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Course Instructions

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Welcome to the Course: Remote Sensing Data Acquisition, Analysis and Applications

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  • Course Introduction

Biography of the Instructor

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  • Instructor biography

Text of Slide Audios for Module 1

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Solutions to End of Lecture Self Checking Quiz Questions for Module 1

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Welcome to Module 1: Acquiring images and understanding how they can be analysed

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1 Videos

  • Welcome to Module 1

Week 1 Lectures and Quiz

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4 Videos

  • 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?

Week 1 Quiz

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Week 2 Lectures

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4 Videos

  • 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

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Week 3 Lectures

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5 Videos

  • 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

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Week 4 Lectures

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4 Videos

  • 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

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Week 5 Lectures

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3 Videos

  • 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

Week 5 Quiz

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Module 1 Test

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  • Module 1 Test questions and your answers

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  • Instructions for test and data to be used when answering questions

Text of Slide Audios for Module 2

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Solutions to End of Lecture Self-Checking Quiz Questions for Module 2

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Welcome to Module 2: Computer-based interpretation – fundamentals of machine learning

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  • Welcome to Module 2

Week 6 lectures

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4 Videos

  • 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

Week 6 Quiz

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Week 7 Lectures

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6 Videos

  • 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

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Week 8 Lectures

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3 Videos

  • 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

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Week 9 Lectures

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5 Videos

  • 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

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Week 10 Lectures

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4 Videos

  • 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"

Week 10 Quiz

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Module 2 Test

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  • Module 2 Test questions and your answers

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Text of Slide Audios for Module 3

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Solutions to End of Lecture Self-Checking Quiz Questions for Module 3

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Welcome to Module 3: Computer-based interpretation in practice, and remote sensing with imaging radar

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1 Videos

  • Welcome to Module 3

Week 11 Lectures

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5 Videos

  • 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

Week 11 Quiz

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Week 12 Lectures

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5 Videos

  • 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

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Week 13 Lectures

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4 Videos

  • 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

13 Quiz

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Week 14 Lectures

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4 Videos

  • 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

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Untitled Lesson

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6 Videos

  • 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

Week 15 Quiz

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Module 3 Test

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  • Module 3 Test questions and your answers

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  • Instructions for test and data to be used when answering questions

Course Conclusion

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  • Course Closing Comments

Auto Summary

Explore the fundamentals of remote sensing with this in-depth course designed for science and engineering enthusiasts. Learn about imaging the earth's surface, sensor types, and computational algorithms, including deep learning techniques. No prior knowledge required, but expect senior undergraduate-level material with vector, matrix algebra, and statistics. The course is extensively illustrated, preparing participants for practical applications and advanced study. Available on Coursera, with flexible starter and professional subscriptions. Perfect for professionals and learners aiming to deepen their expertise in remote sensing.

John Richards
Instructor

John Richards