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Название: Remote Sensing Digital Image Analysis, 6th Edition
Автор: John A. Richards
Издательство: Springer
Год: 2022
Страниц: 576
Язык: английский
Формат: pdf (true)
Размер: 12.2 MB
Remote Sensing Digital Image Analysis provides a comprehensive treatment of the methods used for the processing and interpretation of remotely sensed image data. Over the past decade there have been continuing and significant developments in the algorithms used for the analysis of remote sensing imagery, even though many of the fundamentals have substantially remained the same. As with its predecessors this new edition again presents material that has retained value but also includes newer techniques, covered from the perspective of operational remote sensing. Although the principal focus of the treatment is on digital image interpretation and the analytical techniques that make that possible, the material is located within the domain of remote sensing applications. That means project objectives are as important as finding the best-performing algorithm. Algorithms need to be incorporated into methodologies that can generate optimal results from a careful combination of procedures, and in which the steps of choosing reference material to support the process and for assessing accuracy, may be just as important as algorithm performance. While algorithm performance is a key objective in the Machine Learning (ML) remote sensing research community, it is project outcomes that drive the remote sensing applications specialist.
Автор: John A. Richards
Издательство: Springer
Год: 2022
Страниц: 576
Язык: английский
Формат: pdf (true)
Размер: 12.2 MB
Remote Sensing Digital Image Analysis provides a comprehensive treatment of the methods used for the processing and interpretation of remotely sensed image data. Over the past decade there have been continuing and significant developments in the algorithms used for the analysis of remote sensing imagery, even though many of the fundamentals have substantially remained the same. As with its predecessors this new edition again presents material that has retained value but also includes newer techniques, covered from the perspective of operational remote sensing. Although the principal focus of the treatment is on digital image interpretation and the analytical techniques that make that possible, the material is located within the domain of remote sensing applications. That means project objectives are as important as finding the best-performing algorithm. Algorithms need to be incorporated into methodologies that can generate optimal results from a careful combination of procedures, and in which the steps of choosing reference material to support the process and for assessing accuracy, may be just as important as algorithm performance. While algorithm performance is a key objective in the Machine Learning (ML) remote sensing research community, it is project outcomes that drive the remote sensing applications specialist.