4.6
stars
97 ratings

John Richards

10,428 already enrolled

Offered By

UNSW Sydney (The University of New South Wales)

IEEE Geoscience and Remote Sensing Society

Remote Sensing Image Acquisition, Analysis and Applications

UNSW Sydney (The University of New South Wales)

About this Course

22,250 recent views

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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Fundamental understanding of vector and matrix algebra and basic statistics

Approx. 22 hours to complete

English

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Skills you will gain

Radar Systems
Remote Sensing
Machine Learning
Image Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Fundamental understanding of vector and matrix algebra and basic statistics

Approx. 22 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.45/5 (49 Ratings)

John Richards

Offered by

UNSW Sydney (The University of New South Wales)

UNSW Sydney, based in Sydney Australia, was established in 1949 and is one of Australia’s leading research and teaching universities with more than 50,000 students from over 120 countries. UNSW Sydney aspires to provide students with an outstanding educational experience, which both reflects our strong traditions of excellence, innovation and social justice, and builds on our strengths in scientific, technological and professional disciplines.

IEEE Geoscience and Remote Sensing Society

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Syllabus - What you will learn from this course

Content Rating99%(2,470 ratings)

Week1

Week 1

2 hours to complete

Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz

Remote sensing is the science and technology of acquiring images of the earth’s surface from spacecraft, aircraft and drones to aid in the monitoring and management of the natural and built environments. Extensive computer-based analysis techniques are used to extract information from the recorded images in support of applications ranging over many earth and information science disciplines. 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. The course material is extensively illustrated by examples and commentary on the how the technology is applied in practice. While broad in its coverage the 15 hours of instruction, supported by quizzes and tests, will prepare participants to use the material in their own disciplines and to undertake more detailed study in remote sensing and related topics.

2 hours to complete

6 videos (Total 45 min), 4 readings, 1 quiz

6 videos

Course Introduction7mWelcome to Module 11mModule 1 Lecture 1 What is remote sensing8mModule 1 Lecture 2 The atmosphere8mModule 1 Lecture 3 What platforms are used for imaging the earth's surface?9mModule 1 Lecture 4 How do we record images of the earth's surface?9m

4 readings

Course instructions10mInstructor biography10mText of slide audio files for Module 110mEnd-of-lecture quiz answers10m

1 practice exercise

Week 1 Quiz30m

Week2

Week 2

1 hour to complete

Week 2 Lectures and Quiz

1 hour to complete

4 videos (Total 42 min)

Week3

Week 3

1 hour to complete

Week 3 Lectures and Quiz

1 hour to complete

5 videos (Total 38 min)

Week4

Week 4

1 hour to complete

Week 4 Lectures and Quiz

1 hour to complete

4 videos (Total 38 min)

Week5

Week 5

2 hours to complete

Week 5 Lectures and Quiz, Module 1 Test

2 hours to complete

3 videos (Total 32 min), 1 reading, 2 quizzes

Week6

Week 6

1 hour to complete

Module 2 Introduction, Week 6 lectures and Quiz

1 hour to complete

5 videos (Total 37 min), 2 readings, 1 quiz

5 videos

Welcome to Module 21mModule 2 Lecture 1: Fundamentals of image analysis and machine learning10mModule 2 Lecture 2: The maximum likelihood classifier10mModule 2 Lecture 3: The maximum likelihood classifier—discriminant function and example10mModule 2 Lecture 4: The minimum distance classifier, background material3m

2 readings

Text of slide audio file for Module 210mEnd of lecture quiz solutions10m

1 practice exercise

Week 6 Quiz30m

Week7

Week 7

1 hour to complete

Week 7 Lectures and Quiz

1 hour to complete

6 videos (Total 39 min)

6 videos

Module 2 Lecture 5: Training a linear classifier7mModule 2 Lecture 6: The support vector machine—training12mModule 2 Lecture 7: The support vector machine—the classification step and overlapping data6mModule 2 Lecture 8: The support vector machine—non-linear data3mModule 2 Lecture 9: The support vector machine—multiple classes and the classification step4mModule 2 Lecture 10: The support vector machine—an example4m

1 practice exercise

Week 7 Quiz30m

Week8

Week 8

1 hour to complete

Week 8 Lectures and Quiz

1 hour to complete

3 videos (Total 27 min)

Week9

Week 9

1 hour to complete

Week 9 Lectures and Quiz

1 hour to complete

5 videos (Total 38 min)

Week10

Week 10

2 hours to complete

Week 10 Lectures and Quiz, Module 2 Test

2 hours to complete

4 videos (Total 39 min), 1 reading, 2 quizzes

Week11

Week 11

2 hours to complete

Module 3 Introduction, Week 11 Lectures and Quiz

2 hours to complete

6 videos (Total 59 min), 2 readings, 1 quiz

6 videos

Welcome to Module 32mModule 3 Lecture 1: Feature reduction10mModule 3 Lecture 2: Exploiting the structure of the covariance matrix10mModule 3 Lecture 3: Feature reduction by transformation8mModule 3 Lecture 4: Separability measures12mModule 3 Lecture 5: Distribution-free separability measures14m

2 readings

Text of slide audio file for Module 310mEnd of lecture quiz solutions10m

1 practice exercise

Week 11 Quiz30m

Week12

Week 12

1 hour to complete

Week 12 Lectures and Quiz

1 hour to complete

5 videos (Total 58 min)

Week13

Week 13

1 hour to complete

Week 13 Lectures and Quiz

1 hour to complete

4 videos (Total 40 min)

Week14

Week 14

1 hour to complete

Week 14 Lectures and Quiz

1 hour to complete

4 videos (Total 38 min)

Week15

Week 15

3 hours to complete

Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion

3 hours to complete

7 videos (Total 54 min), 1 reading, 2 quizzes

7 videos

Module 3 Lecture 19: Geometric distortions in radar imagery7mModule 3 Lecture 20: Geometric distortions in radar imagery, cont.9mModule 3 Lecture 21: Radar interferometry9mModule 3 Lecture 22: Radar interferometry for detecting change6mModule 3 Lecture 23: Some other considerations in radar remote sensing11mModule 3 Lecture 24: The course in review6mCourse Closing Comments1m

1 reading

Instructions for test and data to be used when answering questions10m

2 practice exercises

Week 15 Quiz30mModule 3 Test questions and your answers1h

Reviews

4.6

32 reviews

5 stars
72.16%
4 stars
19.58%
3 stars
5.15%
2 stars
3.09%

TOP REVIEWS FROM REMOTE SENSING IMAGE ACQUISITION, ANALYSIS AND APPLICATIONS

by AOJun 26, 2022

This is a great course for inisiation in the remote sensing field of study.

by KVJun 12, 2021

The course should add practice exercise for gaining more understanding to the course

by HVNov 22, 2021

Really enjoyed the learning experience and was very concise!

by ASSep 12, 2022

Very Good Course for having insightful knowledge on VArious Algorithm

View all reviews

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Remote Sensing Image Acquisition, Analysis and Applications

Remote Sensing Image Acquisition, Analysis and Applications

About this Course

Skills you will gain