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Change Detection and Image Time Series Analysis, Volume 1: Unsupervised Methods by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone (Editors) is a nonfiction available to read on EtoBox.

What is Change Detection and Image Time Series Analysis, Volume 1: Unsupervised Methods about?

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

Who reads Change Detection and Image Time Series Analysis, Volume 1: Unsupervised Methods?

Author

Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone (Editors)

Publisher

ISTE-Wiley, John Wiley & Sons

Published

2022

Language

EN

ISBN

9781119882251

Category

nonfiction

Subjects

Computer Science, Artificial Intelligence (Ai), Stem