Remote Sensing Image Classification in R, Kamusoko Courage

Автор: Manjunath V. Joshi
Название: Multi-resolution Image Fusion in Remote Sensing
ISBN: 1108475124 ISBN-13(EAN): 9781108475129
Издательство: Cambridge Academ
Рейтинг:
Цена: 11246.00 р.
Наличие на складе: Поставка под заказ.

Описание: Presenting new advances in the field, this text will be a valuable reference for the students and researchers of image processing, multi-spectral imaging and remote sensing. It discusses tools and techniques of multi resolution image fusion with the necessary mathematical background.

Автор: John A. Richards
Название: Remote Sensing Digital Image Analysis
ISBN: 3642441017 ISBN-13(EAN): 9783642441011
Издательство: Springer
Рейтинг:
Цена: 11179.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Now in an updated edition that adds new and revised material, this book offers a comprehensive introduction to quantitative evaluation of satellite and aircraft derived remotely retrieved data. Each chapter includes practice problems.

Автор: P. Swarnalatha, Prabu Sevugan
Название: Big Data Analytics for Satellite Image Processing and Remote Sensing
ISBN: 1522536434 ISBN-13(EAN): 9781522536437
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 31324.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: The scope of image processing and recognition has broadened due to the gap in scientific visualization. Thus, new imaging techniques have developed, and it is imperative to study this progression for optimal utilization.Big Data Analytics for Satellite Image Processing and Remote Sensing is a critical scholarly resource that examines the challenges and difficulties of implementing big data in image processing for remote sensing and related areas. Featuring coverage on a broad range of topics, such as distributed computing, parallel processing, and spatial data, this book is geared towards scientists, professionals, researchers, and academicians seeking current research on the use of big data analytics in satellite image processing and remote sensing.

Автор: Kumar, Anil , Kumar, A. Senthil , Upadhyay, Priy
Название: Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification
ISBN: 036735571X ISBN-13(EAN): 9780367355715
Издательство: Taylor&Francis
Рейтинг:
Цена: 15004.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. All algorithms are supported by in-house developed tool as SMIC.

Автор: Ioannis Kanellopoulos; Graeme G. Wilkinson; Theo M
Название: Machine Vision and Advanced Image Processing in Remote Sensing
ISBN: 3642642608 ISBN-13(EAN): 9783642642609
Издательство: Springer
Рейтинг:
Цена: 19591.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Автор: Steven M. de Jong; Freek D. van der Meer
Название: Remote Sensing Image Analysis: Including the Spatial Domain
ISBN: 9401740615 ISBN-13(EAN): 9789401740616
Издательство: Springer
Рейтинг:
Цена: 13060.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Including a CD-ROM with colour images printed in B&W in the book itself

Автор: Gabriele Moser; Josiane Zerubia
Название: Mathematical Models for Remote Sensing Image Processing
ISBN: 3319663283 ISBN-13(EAN): 9783319663289
Издательство: Springer
Рейтинг:
Цена: 22359.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book maximizes reader insights into the field of mathematical models and methods for the processing of two-dimensional remote sensing images. It presents a broad analysis of the field, encompassing passive and active sensors, hyperspectral images, synthetic aperture radar (SAR), interferometric SAR, and polarimetric SAR data. At the same time, it addresses highly topical subjects involving remote sensing data types (e.g., very high-resolution images, multiangular or multiresolution data, and satellite image time series) and analysis methodologies (e.g., probabilistic graphical models, hierarchical image representations, kernel machines, data fusion, and compressive sensing) that currently have primary importance in the field of mathematical modelling for remote sensing and image processing. Each chapter focuses on a particular type of remote sensing data and/or on a specific methodological area, presenting both a thorough analysis of the previous literature and a methodological and experimental discussion of at least two advanced mathematical methods for information extraction from remote sensing data. This organization ensures that both tutorial information and advanced subjects are covered. With each chapter being written by research scientists from (at least) two different institutions, it offers multiple professional experiences and perspectives on each subject. The book also provides expert analysis and commentary from leading remote sensing and image processing researchers, many of whom serve on the editorial boards of prestigious international journals in these fields, and are actively involved in international scientific societies. Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of mathematical models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners.

Автор: Jacqueline Le Moigne, Nathan S. Netanyahu, Roger D. Eastman
Название: Image Registration for Remote Sensing
ISBN: 1108445756 ISBN-13(EAN): 9781108445757
Издательство: Cambridge Academ
Рейтинг:
Цена: 9346.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet`s surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

Автор: Gabriele Moser; Josiane Zerubia
Название: Mathematical Models for Remote Sensing Image Processing
ISBN: 3319882198 ISBN-13(EAN): 9783319882192
Издательство: Springer
Рейтинг:
Цена: 22359.00 р.
Наличие на складе: Поставка под заказ.

Описание: This book maximizes reader insights into the field of mathematical models and methods for the processing of two-dimensional remote sensing images. It presents a broad analysis of the field, encompassing passive and active sensors, hyperspectral images, synthetic aperture radar (SAR), interferometric SAR, and polarimetric SAR data. At the same time, it addresses highly topical subjects involving remote sensing data types (e.g., very high-resolution images, multiangular or multiresolution data, and satellite image time series) and analysis methodologies (e.g., probabilistic graphical models, hierarchical image representations, kernel machines, data fusion, and compressive sensing) that currently have primary importance in the field of mathematical modelling for remote sensing and image processing. Each chapter focuses on a particular type of remote sensing data and/or on a specific methodological area, presenting both a thorough analysis of the previous literature and a methodological and experimental discussion of at least two advanced mathematical methods for information extraction from remote sensing data. This organization ensures that both tutorial information and advanced subjects are covered. With each chapter being written by research scientists from (at least) two different institutions, it offers multiple professional experiences and perspectives on each subject. The book also provides expert analysis and commentary from leading remote sensing and image processing researchers, many of whom serve on the editorial boards of prestigious international journals in these fields, and are actively involved in international scientific societies. Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of mathematical models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners.