Change Detection and Image Time-Series Analysis 1

Unervised Methods

Inbunden, Engelska, 2022

Av Abdourrahmane M. Atto, Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone, Abdourrahmane M Atto

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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.

Produktinformation

Utgivningsdatum2022-01-04
Mått10 x 10 x 10 mm
Vikt454 g
FormatInbunden
SpråkEngelska
Antal sidor304
FörlagISTE Ltd
ISBN9781789450569

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Systemvetenskap och AI inom Data och it

Abdourrahmane M. Atto is Associate Professor at the University Savoie Mont Blanc, France. His research interests include mathematical methods and models for artificial intelligence and image time series. Francesca Bovolo is the Head of the Remote Sensing for Digital Earth Unit, Fondazione Bruno Kessler, Italy. Her research interests include remote sensing image time series analysis, content-based time series retrieval and radar sounders. Lorenzo Bruzzone is Professor of Telecommunications and the Founder and Director of the Remote Sensing Laboratory at the University of Trento, Italy. His research interests include remote sensing, machine learning and pattern recognition.

ContentsPreface xiAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONEList of NotationsChapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1Sicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDUand Xiaohua TONG1.1. Introduction 11.2. Unsupervised change detection in multispectral images 31.2.1.Relatedconcepts 31.2.2.Openissuesandchallenges 71.2.3. Spectral–spatial unsupervised CD techniques 71.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 91.3.1 Sequential spectral change vector analysis (S 2 CVA) 91.3.2. Multiscale morphological compressed change vector analysis 111.3.3. Superpixel-level compressed change vector analysis 151.4.Datasetdescriptionandexperimentalsetup 181.4.1.Datasetdescription 181.4.2.Experimentalsetup 221.5.Resultsanddiscussion 241.5.1.ResultsontheXuzhoudataset 241.5.2. Results on the Indonesia tsunami dataset 24xv1.6.Conclusion 281.7.Acknowledgements 291.8.References 29Chapter 2 Change Detection in Time Series of Polarimetric SAR Images 35Knut CONRADSEN, Henning SKRIVER, MortonJ.CANTYandAllanA.NIELSEN2.1. Introduction 352.1.1.Theproblem 362.1.2 Important concepts illustrated by means of the gamma distribution 392.2.Testtheoryandmatrixordering 452.2.1. Test for equality of two complex Wishart distributions 452.2.2. Test for equality of k-complex Wishart distributions 472.2.3. The block diagonal case 492.2.4.TheLoewnerorder 522.3.Thebasicchangedetectionalgorithm 532.4.Applications 552.4.1.Visualizingchanges 582.4.2.Fieldwisechangedetection 592.4.3. Directional changes using the Loewner ordering 622.4.4. Software availability 652.5.References 70Chapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73Ammar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ,Arnaud BRELOY and Frédéric PASCAL3.1. Introduction 733.2.Datasetdescription 763.3.StatisticalmodelingofSARimages 773.3.1.Thedata 773.3.2.Gaussianmodel 773.3.3.Non-Gaussianmodeling 833.4.Dissimilaritymeasures 843.4.1.Problemformulation 843.4.2. Hypothesis testing statistics 853.4.3.Information-theoreticmeasures 873.4.4.Riemanniangeometrydistances 893.4.5.Optimaltransport 903.4.6.Summary 913.4.7. Results of change detectors on the UAVSAR dataset 913.5. Change detection based on structured covariances 943.5.1. Low-rank Gaussian change detector 963.5.2. Low-rank compound Gaussian change detector 973.5.3. Results of low-rank change detectors on the UAVSAR dataset 1003.6.Conclusion 1023.7.References 103Chapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109Abdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISINand Lionel BOMBRUN4.1. Introduction 1094.2.Parametricmodelingofconvnetfeatures 1104.3.Anomalydetectioninimagetimeseries 1134.4.Functionalimagetimeseriesclustering 1194.5.Conclusion 1234.6.References 123Chapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127Fatima KARBOU, Guillaume JAMES, Philippe DURANDand Abdourrahmane M. ATTO5.1. Introduction 1275.2.Testareaanddata 1295.3.WetsnowdetectionusingSentinel-1 1295.4.Metricstodetectwetsnow 1335.5.Discussion 1385.6.Conclusion 1435.7.Acknowledgements 1435.8.References 143Chapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145Abdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHACand Pedro MORETTIN6.1. Introduction 1456.2. Random field model of a cyclone texture 1486.2.1.Cyclonetexturefeature 1496.2.2. Wavelet-based power spectral densities and cyclone fields 1506.2.3. Fractional spectral power decay model 1536.3.Cyclonefieldeyedetectionandtracking 1576.3.1.Cycloneeyedetection 1576.3.2.Dynamicfractalfieldeyetracking 1586.4. Cyclone field intensity evolution prediction 1596.5.Discussion 1616.6.Acknowledgements 1636.7.References 163Chapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167Minh-Tan PHAM and Grégoire MERCIER7.1. Introduction 1677.2. Texture representation and characterization using local extrema 1697.2.1.Motivationandapproach 1697.2.2. Local extrema keypoints within SAR images 1727.3.Unsupervisedchangedetection 1757.3.1. Proposed framework 1757.3.2. Weighted graph construction from keypoints 1767.3.3.Changemeasure(CM)generation 1787.4.Experimentalstudy 1797.4.1. Data description and evaluation criteria 1797.4.2.Changedetectionresults 1817.4.3.Sensitivitytoparameters 1857.4.4.ComparisonwiththeNLMmodel 1887.4.5. Analysis of the algorithm complexity 1917.5.Applicationtoglacierflowmeasurement 1927.5.1. Proposed method 1937.5.2.Results 1947.6.Conclusion 1967.7.References 197Chapter 8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale 201Andrea GARZELLI and Claudia ZOPPETTI8.1. Introduction 2018.2. Proposed method 2038.2.1.Testsiteanddata 2068.3.SARprocessing 2098.4.Opticalprocessing 2158.5.Combinationlayer 2178.6.Results 2198.7.Conclusion 2208.8.References 221Chapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223Massimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE9.1. Introduction 2239.2. Overview of the change detection problem 2259.2.1. Change detection methods for multispectral images 2279.2.2. Challenges addressed in this chapter 2309.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 2319.3.1. Magnitude image statistical mixture model 2319.3.2.Bayesiandecision 2339.3.3. Numerical approach to parameter estimation 2349.4. A compound multiclass statistical model of the difference image 2399.4.1. Difference image statistical mixture model 2409.4.2. Magnitude image statistical mixture model 2459.4.3.Bayesiandecision 2489.4.4. Numerical approach to parameter estimation 2499.5.Experimentalresults 2539.5.1.Datasetdescription 2539.5.2.Experimentalsetup 2569.5.3. Test 1: Two-class Rayleigh–Rice mixture model 2569.5.4. Test 2: Multiclass Rician mixture model 2609.6.Conclusion 2669.7.References 267List of Authors 275Index 277Summary of Volume 2 281