Name: Change Detection and Image Time-Series Analysis 1: Unervised Methods (en Inglés)
SKU: 56147132
Price: 182.92 USD
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Author: Atto Abdourrahmane M.,Bovolo Francesca,Bruzzone Lorenzo

portada Change Detection and Image Time-Series Analysis 1: Unervised Methods (en Inglés)

Change Detection and Image Time-Series Analysis 1: Unervised Methods (en Inglés)

Formato

Libro Físico

Editorial

Iste Ltd

Autor

Atto Abdourrahmane M.,Bovolo Francesca,Bruzzone Lorenzo

Idioma

Inglés

N° páginas

304

Encuadernación

Tapa Dura

ISBN13

9781789450569

N° edición

Change Detection and Image Time-Series Analysis 1: Unervised Methods (en Inglés)

Atto Abdourrahmane M.,Bovolo Francesca,Bruzzone Lorenzo (Autor) · Iste Ltd · Tapa Dura

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Reseña del libro "Change Detection and Image Time-Series Analysis 1: Unervised Methods (en Inglés)"

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.