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Remote Sensing Image Acquisition, Analysis and Applications · LearnSpace
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Remote Sensing Image Acquisition, Analysis and Applications

Курс от UNSW Sydney (The University of New South Wales), IEEE Geoscience and Remote Sensing Society
Средний≈ 22.6 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Image AnalysisDimensionality ReductionSupervised LearningImage QualityConvolutional Neural NetworksUnsupervised LearningMachine Learning AlgorithmsEngineering, Scientific, and Technical InstrumentsComputer VisionEnvironmental MonitoringSpatial AnalysisSpatial Data AnalysisFeature EngineeringApplied Machine LearningExploratory Data AnalysisStatistical AnalysisMachine Learning MethodsGeospatial Information and TechnologyStatistical MethodsMachine Learning

Программа курса

15 модулей · 100 учебных материалов

01Course Welcome, Instructor, Course Resources, Module 1 Introduction and Week 1 Lectures and Quiz11 материалов

Course Instructions

Course instructionsЧтение

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

Course IntroductionВидео

Biography of the Instructor

Instructor biographyЧтение

Text of Slide Audios for Module 1

Учитесь у экспертов

John Richards

Emeritus Professor

Remote Sensing Image Acquisition, Analysis and Applications
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Обучение на Coursera

≈ 22.6 ч

15 модулей

Язык: Английский

Субтитры: Арабский, Французский, Украинский, Китайский (Китай), Греческий, Итальянский, Бразильский португальский, Нидерландский, Корейский, Немецкий, Русский, Тайский, Индонезийский, Шведский, Турецкий, Испанский, Хинди, Японский, Казахский, Польский

Часть программы вашего университета
Text of slide audio file for Module 1Чтение

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

End of lecture quiz solutionsЧтение

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

Welcome to Module 1Видео

Week 1 Lectures and Quiz

Module 1 Lecture 1 What is remote sensingВидеоModule 1 Lecture 2 The atmosphereВидеоModule 1 Lecture 3 What platforms are used for imaging the earth's surface?ВидеоModule 1 Lecture 4 How do we record images of the earth's surface?Видео

Week 1 Quiz

Week 1 QuizЗадание
02Week 2 Lectures and Quiz5 материалов

Week 2 Lectures

Module 1 Lecture 5 What are we trying to measure?ВидеоModule 1 Lecture 6 Distortions in recorded imagesВидеоModule 1 Lecture 7 Geometric distortion in recorded imagesВидеоModule 1 Lecture 8 Correcting geometric distortionВидео

Week 2 Quiz

Week 2 QuizЗадание
03Week 3 Lectures and Quiz6 материалов

Week 3 Lectures

Module 1 Lecture 9 Correcting geometric distortion using mapping functions and control pointВидеоModule 1 Lecture 10 ResamplingВидеоModule 1 Lecture 11 An image registration exampleВидеоModule 1 Lecture 12 How can images be interpreted and used?ВидеоModule 1 Lecture 13 Enhancing image contrastВидео

Week 3 Quiz

Week 3 QuizЗадание
04Week 4 Lectures and Quiz5 материалов

Week 4 Lectures

Module 1 Lecture 14 An introduction to classification (quantitative analysis)ВидеоModule 1 Lecture 15 Classification: some more detailВидеоModule 1 Lecture 16 Correlation and covarianceВидеоModule 1 Lecture 17 The principal components transformВидео

Week 4 Quiz

Week 4 QuizЗадание
05Week 5 Lectures and Quiz, Module 1 Test6 материалов

Week 5 Lectures

Module 1 Lecture 18 The principal components transform: worked exampleВидеоModule 1 Lecture 19 The principal components transform: a real exampleВидеоModule 1 Lecture 20 Applications of the principal components transformВидео

Week 5 Quiz

Week 5 QuizЗадание

Module 1 Test

Module 1 Test Instructions and Reference DataЧтениеModule 1 Test questions and your answersЗадание
06Module 2 Introduction, Week 6 lectures and Quiz8 материалов

Text of Slide Audios for Module 2

Text of slide audio file for Module 2Чтение

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

End of lecture quiz solutionsЧтение

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

Welcome to Module 2Видео

Week 6 lectures

Module 2 Lecture 1: Fundamentals of image analysis and machine learningВидеоModule 2 Lecture 2: The maximum likelihood classifierВидеоModule 2 Lecture 3: The maximum likelihood classifier—discriminant function and exampleВидеоModule 2 Lecture 4: The minimum distance classifier, background materialВидео

Week 6 Quiz

Week 6 QuizЗадание
07Week 7 Lectures and Quiz7 материалов

Week 7 Lectures

Module 2 Lecture 5: Training a linear classifierВидеоModule 2 Lecture 6: The support vector machine—trainingВидеоModule 2 Lecture 7: The support vector machine—the classification step and overlapping dataВидеоModule 2 Lecture 8: The support vector machine—non-linear dataВидеоModule 2 Lecture 9: The support vector machine—multiple classes and the classification stepВидеоModule 2 Lecture 10: The support vector machine—an exampleВидео

Week 7 Quiz

Week 7 QuizЗадание
08Week 8 Lectures and Quiz4 материалов

Week 8 Lectures

Module 2 Lecture 11: The neural network as a classifierВидеоModule 2 Lecture 12: Training the neural networВидеоModule 2 Lecture 13: Neural network examplesВидео

Week 8 Quiz

Week 8 QuizЗадание
09Week 9 Lectures and Quiz6 материалов

Week 9 Lectures

Module 2 Lecture 14: Deep learning and the convolutional neural network, part 1ВидеоModule 2 Lecture 15: Deep learning and the convolutional neural network, part 2ВидеоModule 2 Lecture 16: Deep learning and the convolutional neural network, part 3ВидеоModule 2 Lecture 17: CNN examples in remote sensingВидеоModule 2 Lecture 18: Comparing the classsifiersВидео

Week 9 Quiz

Week 9 QuizЗадание
10Week 10 Lectures and Quiz, Module 2 Test7 материалов

Week 10 Lectures

Module 2 Lecture 19: Unsupervised classification and clusteringВидеоModule 2 Lecture 20: Examples of k means clusteringВидеоModule 2 Lecture 21: Other clustering methodsВидеоModule 2 Lecture 22: Clustering "big data"Видео

Week 10 Quiz

Week 10 QuizЗадание

Module 2 Test

Module 2 Test Instructions and Reference DataЧтениеModule 2 Test questions and your answersЗадание
11Module 3 Introduction, Week 11 Lectures and Quiz9 материалов

Text of Slide Audios for Module 3

Text of slide audio file for Module 3Чтение

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

End of lecture quiz solutionsЧтение

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

Welcome to Module 3Видео

Week 11 Lectures

Module 3 Lecture 1: Feature reductionВидеоModule 3 Lecture 2: Exploiting the structure of the covariance matrixВидеоModule 3 Lecture 3: Feature reduction by transformationВидеоModule 3 Lecture 4: Separability measuresВидеоModule 3 Lecture 5: Distribution-free separability measuresВидео

Week 11 Quiz

Week 11 QuizЗадание
12Week 12 Lectures and Quiz6 материалов

Week 12 Lectures

Module 3 Lecture 6: Assessing classifier performance and map errorsВидеоModule 3 Lecture 7: Classifier performance and map accuracyВидеоModule 3 Lecture 8: Choosing testing pixels for assessing map accuracyВидеоModule 3 Lecture 9: Classification methodologiesВидеоModule 3 Lecture 10: Other interpretation methodsВидео

Week 12 Quiz

Week 12 QuizЗадание
13Week 13 Lectures and Quiz5 материалов

Week 13 Lectures

Module 3 Lecture 11: Fundamentals of radar imagingВидеоModule 3 lecture 12: Summary of SAR and its practical implicationsВидеоModule 3 Lecture 13: The scattereing coefficientВидеоModule 3 Lecture 14: Speckle and an introduction to scattering mechanismsВидео

13 Quiz

Week 13 QuizЗадание
14Week 14 Lectures and Quiz5 материалов

Week 14 Lectures

Module 3 Lecture 15: Radar scattering from the earth's surfaceВидеоModule 3 Lecture 16: Sub-surface imaging and volume scatteringВидеоModule 3 Lecture 17: Scattering from hard targetsВидеоModule 3 Lecture 18: The cardinal effect, Bragg scattering and scattering from the seaВидео

Week 14 Quiz

Week 14 QuizЗадание
15Week 15 Lectures and Quiz, Module 3 Test, Course Conclusion10 материалов

Week 15 Lectures

Module 3 Lecture 19: Geometric distortions in radar imageryВидеоModule 3 Lecture 20: Geometric distortions in radar imagery, cont.ВидеоModule 3 Lecture 21: Radar interferometryВидеоModule 3 Lecture 22: Radar interferometry for detecting changeВидеоModule 3 Lecture 23: Some other considerations in radar remote sensingВидеоModule 3 Lecture 24: The course in reviewВидео

Week 15 Quiz

Week 15 QuizЗадание

Module 3 Test

Module 3 Test Instructions and Reference DataЧтениеModule 3 Test questions and your answersЗадание

Course Conclusion

Course Closing CommentsВидео