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University of New South Wales

Remote Sensing Image Acquisition, Analysis and Applications

University of New South Wales and IEEE Geoscience and Remote Sensing Society via Coursera

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Overview

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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.

Syllabus

  • Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz
    • This week, you will be introduced to the foundations of remote sensing and the processes used to acquire images of the Earth’s surface. You will explore how the atmosphere influences image formation, examine the platforms and sensors used in remote sensing, and develop an understanding of how remotely sensed imagery is collected and used in practice.
  • Week 2 Lectures and Quiz
    • This week, you will examine what remote sensing instruments measure and how image quality can be affected by radiometric and geometric distortions. You will learn how these errors arise and explore techniques used to correct them, helping ensure that remotely sensed images accurately represent real-world conditions.
  • Week 3 Lectures and Quiz
    • This week, you will learn how image geometry is corrected using control points, resampling techniques, and image-to-image registration methods. You will also explore image interpretation and contrast enhancement approaches that improve the usability and visual quality of remote sensing data.
  • Week 4 Lectures and Quiz
    • This week, you will explore the fundamentals of image classification and thematic mapping. You will examine covariance and correlation matrices and learn how principal components analysis can be used to reduce data complexity while preserving important information for image analysis.
  • Week 5 Lectures and Quiz, Module 1 Test
    • This week, you will focus on the practical application of principal components analysis in remote sensing. Through worked examples and real-world use cases, you will learn how this technique supports image interpretation, feature extraction, and more efficient analysis of complex datasets.
  • Module 2 Introduction, Week 6 lectures and Quiz
    • This week, you will be introduced to the foundations of computer-based image interpretation and machine learning for remote sensing. You will explore the principles of supervised classification, examine how maximum likelihood classifiers make classification decisions, and learn how discrimination functions are used to separate image classes. You will also compare classification approaches and develop an understanding of how machine learning techniques support automated image analysis.
  • Week 7 Lectures and Quiz
    • This week, you will learn how linear classifiers are trained and applied to remote sensing data. You will explore the development of support vector machines (SVMs), understand how they classify both linear and non-linear datasets, and examine practical examples of SVM implementation.
  • Week 8 Lectures and Quiz
    • This week, you will examine the structure and operation of neural networks as classification tools. You will learn how neural networks are trained, how they identify patterns in data, and how they can be applied to solve remote sensing classification problems.
  • Week 9 Lectures and Quiz
    • This week, you will explore the evolution from traditional neural networks to convolutional neural networks (CNNs). You will learn how CNNs are structured, how they are applied to remote sensing imagery, and how their performance compares with other classification approaches.
  • Week 10 Lectures and Quiz, Module 2 Test
    • This week, you will study unsupervised classification techniques and the role of clustering in identifying patterns within unlabeled data. You will examine common clustering methods, including k-means clustering, and explore approaches designed to handle large-scale remote sensing datasets.
  • Module 3 Introduction, Week 11 Lectures and Quiz
    • This week, you will learn techniques for reducing and selecting features in remote sensing datasets. You will explore transformation-based methods, separability measures, and the differences between distribution-based and distribution-free approaches used to improve classification performance and efficiency.
  • Week 12 Lectures and Quiz
    • This week, you will examine how thematic map accuracy and classifier performance are evaluated. You will learn the distinction between classification accuracy and mapping accuracy and explore methodologies that combine supervised and unsupervised techniques to improve image interpretation outcomes.
  • Week 13 Lectures and Quiz
    • This week, you will be introduced to imaging radar and synthetic aperture radar (SAR). You will explore radar scattering concepts, radar cross sections, and the phenomenon of speckle, gaining a foundation for understanding radar imagery and its applications in remote sensing.
  • Week 14 Lectures and Quiz
    • This week, you will investigate how radar energy interacts with the Earth’s surface. You will examine surface and volume scattering, reflections from hard targets, sea-surface scattering, and the cardinal effect, developing a deeper understanding of radar image interpretation.
  • Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion
    • This week, you will explore advanced radar remote sensing concepts, including geometric distortions, radar calibration, interferometry, and tomography. You will also examine the benefits of combining optical and radar imagery and conclude the course by reviewing key concepts and applications covered throughout the program.

Taught by

John Richards

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Reviews

5.0 rating, based on 1 Class Central review

4.7 rating at Coursera based on 183 ratings

Start your review of Remote Sensing Image Acquisition, Analysis and Applications

  • Profile image for 228W1A12E4_Sec-C Chimata Venkata Akhila
    228W1A12E4_Sec-C Chimata Venkata Akhila
    I recently completed the Remote Sensing course, and it was an excellent introduction to the field! As someone new to satellite imagery and GIS, I appreciated how the course started with the basics—like electromagnetic radiation, sensor types, and pl…
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