Change Detection and Image Time-Series Analysis Volume 1 - Unsupervised Methods (Hardcover)

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.

General

Imprint:	Iste
Country of origin:	United Kingdom
Release date:	2022
First published:	2021
Authors:	A Atto
Dimensions:	244 x 157 x 21mm (L x W x T)
Format:	Hardcover
Pages:	304
ISBN-13:	978-1-78945-056-9
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	1-78945-056-X
Barcode:	9781789450569

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