Описание: This comprehensive new resource brings you up to date on recent developments in the classification of hyperspectral images using both spectral and spatial information, including advanced statistical approaches and methods. The inclusion of spatial information to traditional approaches for hyperspectral classification has been one of the most active and relevant innovative lines of research in remote sensing during recent years. This book gives you insight into several important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. This book also shows you how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide
Автор: Prost, Gary L. Название: Remote Sensing for Geoscientists, 3 ed. ISBN: 0367867575 ISBN-13(EAN): 9780367867577 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 р. Наличие на складе: Поставка под заказ.
Описание: This book is an updated and expanded version of the existing text that explains what remote sensing is and how to use it in the earth sciences. It serves as a how-to guide and reference for those requiring remote sensing to assist with geologic mapping, landform analysis, petroleum and mineral exploration, groundwater development, civil engineer
Автор: Cem ?nsalan; Kim L. Boyer Название: Multispectral Satellite Image Understanding ISBN: 1447126564 ISBN-13(EAN): 9781447126560 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Covering image processing methods for analyzing residential land use, this book combines theoretical framework with practical applications, and describes a high resolution system for effective detection of single houses, vegetation and shadow-water indices.
Автор: Atto Abdourramahn, Bovolo Francesca, Bruzzone Lorenzo Название: Change Detection and Image Time-Series Analysis 1: Unervised Methods ISBN: 178945056X ISBN-13(EAN): 9781789450569 Издательство: Wiley Рейтинг: Цена: 21851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Change Detection and Image Time Series Analysis 1 presents a wide range of unsupervised methods for temporal evolution analysis through the use of image time series associated with optical and/or synthetic aperture radar acquisition modalities.
Chapter 1 introduces two unsupervised approaches to multiple-change detection in bi-temporal multivariate images, with Chapters 2 and 3 addressing change detection in image time series in the context of the statistical analysis of covariance matrices. Chapter 4 focuses on wavelets and convolutional-neural filters for feature extraction and entropy-based anomaly detection, and Chapter 5 deals with a number of metrics such as cross correlation ratios and the Hausdorff distance for variational analysis of the state of snow. Chapter 6 presents a fractional dynamic stochastic field model for spatio temporal forecasting and for monitoring fast-moving meteorological events such as cyclones. Chapter 7 proposes an analysis based on characteristic points for texture modeling, in the context of graph theory, and Chapter 8 focuses on detecting new land cover types by classification-based change detection or feature/pixel based change detection. Chapter 9 focuses on the modeling of classes in the difference image and derives a multiclass model for this difference image in the context of change vector analysis.
Автор: Atto Abdourrahmane M., Bovolo Francesca, Bruzzone Lorenzo Название: Change Detection and Image Time Series Analysis 2: Supervised Methods ISBN: 1789450578 ISBN-13(EAN): 9781789450576 Издательство: Wiley Рейтинг: Цена: 21851.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.
Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.
Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.
Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,
Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.
Автор: Chen Chen; Yuzhuo Ren; C.-C. Jay Kuo Название: Big Visual Data Analysis ISBN: 9811006296 ISBN-13(EAN): 9789811006296 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoorscene classification, and outdoor scene layout estimation.
Автор: Surekha Borra; Rohit Thanki; Nilanjan Dey Название: Satellite Image Analysis: Clustering and Classification ISBN: 9811364230 ISBN-13(EAN): 9789811364235 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time.
This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.
Автор: Kamusoko Courage Название: Remote Sensing Image Classification in R ISBN: 9811380112 ISBN-13(EAN): 9789811380112 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book offers an introduction to remotely sensed image processing and classification in R using machine learning algorithms. It also provides a concise and practical reference tutorial, which equips readers to immediately start using the software platform and R packages for image processing and classification.This book is divided into five chapters. Chapter 1 introduces remote sensing digital image processing in R, while chapter 2 covers pre-processing. Chapter 3 focuses on image transformation, and chapter 4 addresses image classification. Lastly, chapter 5 deals with improving image classification.R is advantageous in that it is open source software, available free of charge and includes several useful features that are not available in commercial software packages. This book benefits all undergraduate and graduate students, researchers, university teachers and other remote- sensing practitioners interested in the practical implementation of remote sensing in R.
Автор: Donald B. Percival, Andrew T. Walden Название: Spectral Analysis for Univariate Time Series ISBN: 1107028140 ISBN-13(EAN): 9781107028142 Издательство: Cambridge Academ Рейтинг: Цена: 14573.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.
Автор: Dengsheng Zhang Название: Fundamentals of Image Data Mining ISBN: 3030179885 ISBN-13(EAN): 9783030179885 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Поставка под заказ.
Описание: This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments.Topics and features: describes the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms; reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining; emphasizes how to deal with real image data for practical image mining; highlights how such features as color, texture, and shape can be mined or extracted from images for image representation; presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees; discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods; provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter.This easy-to-follow work illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
Описание: This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods;
Описание: Introduction.- Hyperspectral Imaging System.- Classification Techniques for HSI.- Preprocessing: Noise Reduction/ Band Categorization for HSI.- Spatial Feature Extraction Using Segmentation.- Multiple Deep learning models for feature extraction in classification.- Deep learning for merging spatial and spectral information in classification.- Sparse cording for Hyperspectral Data.- Classification Applications of HSI classification.- Conclusion.
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