{"product_id":"change-detection-and-image-time-series-analysis-1-unervised-methods-hardback-9781789450569","title":"Change Detection and Image Time-Series Analysis 1; Unervised Methods (Hardback) 9781789450569","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eChange Detection and Image Time-Series Analysis 1\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eUnervised Methods\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eAbdourrahmane M. Atto (Edited by), A Atto (Author), Francesca Bovolo (Edited by), Lorenzo Bruzzone (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781789450569, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 4 January 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e304 pages\u003cbr\u003e1 x 1 x 1 cm, 0.454 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eChange 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. \u003cbr\u003e\u003cbr\u003eChapter 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eContents\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003eList of Notations\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Unsupervised Change Detection in Multitemporal Remote Sensing Images 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSicong LIU, Francesca BOVOLO, Lorenzo BRUZZONE, QianDU\u003c\/p\u003e \u003cp\u003eand Xiaohua TONG\u003c\/p\u003e \u003cp\u003e1.1. Introduction 1\u003c\/p\u003e \u003cp\u003e1.2. Unsupervised change detection in multispectral images 3\u003c\/p\u003e \u003cp\u003e1.2.1.Relatedconcepts 3\u003c\/p\u003e \u003cp\u003e1.2.2.Openissuesandchallenges 7\u003c\/p\u003e \u003cp\u003e1.2.3. Spectral–spatial unsupervised CD techniques 7\u003c\/p\u003e \u003cp\u003e1.3 Unsupervised multiclass change detection approaches based on modelingspectral–spatialinformation 9\u003c\/p\u003e \u003cp\u003e1.3.1 Sequential spectral change vector analysis (S 2 CVA) 9\u003c\/p\u003e \u003cp\u003e1.3.2. Multiscale morphological compressed change vector analysis 11\u003c\/p\u003e \u003cp\u003e1.3.3. Superpixel-level compressed change vector analysis 15\u003c\/p\u003e \u003cp\u003e1.4.Datasetdescriptionandexperimentalsetup 18\u003c\/p\u003e \u003cp\u003e1.4.1.Datasetdescription 18\u003c\/p\u003e \u003cp\u003e1.4.2.Experimentalsetup 22\u003c\/p\u003e \u003cp\u003e1.5.Resultsanddiscussion 24\u003c\/p\u003e \u003cp\u003e1.5.1.ResultsontheXuzhoudataset 24\u003c\/p\u003e \u003cp\u003e1.5.2. Results on the Indonesia tsunami dataset 24\u003c\/p\u003e \u003cp\u003exv\u003c\/p\u003e \u003cp\u003e1.6.Conclusion 28\u003c\/p\u003e \u003cp\u003e1.7.Acknowledgements 29\u003c\/p\u003e \u003cp\u003e1.8.References 29\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Change Detection in Time Series of Polarimetric SAR Images 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eKnut CONRADSEN, Henning SKRIVER, MortonJ.CANTY\u003c\/p\u003e \u003cp\u003eandAllanA.NIELSEN\u003c\/p\u003e \u003cp\u003e2.1. Introduction 35\u003c\/p\u003e \u003cp\u003e2.1.1.Theproblem 36\u003c\/p\u003e \u003cp\u003e2.1.2 Important concepts illustrated by means of the gamma distribution 39\u003c\/p\u003e \u003cp\u003e2.2.Testtheoryandmatrixordering 45\u003c\/p\u003e \u003cp\u003e2.2.1. Test for equality of two complex Wishart distributions 45\u003c\/p\u003e \u003cp\u003e2.2.2. Test for equality of k-complex Wishart distributions 47\u003c\/p\u003e \u003cp\u003e2.2.3. The block diagonal case 49\u003c\/p\u003e \u003cp\u003e2.2.4.TheLoewnerorder 52\u003c\/p\u003e \u003cp\u003e2.3.Thebasicchangedetectionalgorithm 53\u003c\/p\u003e \u003cp\u003e2.4.Applications 55\u003c\/p\u003e \u003cp\u003e2.4.1.Visualizingchanges 58\u003c\/p\u003e \u003cp\u003e2.4.2.Fieldwisechangedetection 59\u003c\/p\u003e \u003cp\u003e2.4.3. Directional changes using the Loewner ordering 62\u003c\/p\u003e \u003cp\u003e2.4.4. Software availability 65\u003c\/p\u003e \u003cp\u003e2.5.References 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series 73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAmmar MIAN, Guillaume GINOLHAC, Jean-Philippe OVARLEZ,\u003c\/p\u003e \u003cp\u003eArnaud BRELOY and Frédéric PASCAL\u003c\/p\u003e \u003cp\u003e3.1. Introduction 73\u003c\/p\u003e \u003cp\u003e3.2.Datasetdescription 76\u003c\/p\u003e \u003cp\u003e3.3.StatisticalmodelingofSARimages 77\u003c\/p\u003e \u003cp\u003e3.3.1.Thedata 77\u003c\/p\u003e \u003cp\u003e3.3.2.Gaussianmodel 77\u003c\/p\u003e \u003cp\u003e3.3.3.Non-Gaussianmodeling 83\u003c\/p\u003e \u003cp\u003e3.4.Dissimilaritymeasures 84\u003c\/p\u003e \u003cp\u003e3.4.1.Problemformulation 84\u003c\/p\u003e \u003cp\u003e3.4.2. Hypothesis testing statistics 85\u003c\/p\u003e \u003cp\u003e3.4.3.Information-theoreticmeasures 87\u003c\/p\u003e \u003cp\u003e3.4.4.Riemanniangeometrydistances 89\u003c\/p\u003e \u003cp\u003e3.4.5.Optimaltransport 90\u003c\/p\u003e \u003cp\u003e3.4.6.Summary 91\u003c\/p\u003e \u003cp\u003e3.4.7. Results of change detectors on the UAVSAR dataset 91\u003c\/p\u003e \u003cp\u003e3.5. Change detection based on structured covariances 94\u003c\/p\u003e \u003cp\u003e3.5.1. Low-rank Gaussian change detector 96\u003c\/p\u003e \u003cp\u003e3.5.2. Low-rank compound Gaussian change detector 97\u003c\/p\u003e \u003cp\u003e3.5.3. Results of low-rank change detectors on the UAVSAR dataset 100\u003c\/p\u003e \u003cp\u003e3.6.Conclusion 102\u003c\/p\u003e \u003cp\u003e3.7.References 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Fatima KARBOU, Sophie GIFFARD-ROISIN\u003c\/p\u003e \u003cp\u003eand Lionel BOMBRUN\u003c\/p\u003e \u003cp\u003e4.1. Introduction 109\u003c\/p\u003e \u003cp\u003e4.2.Parametricmodelingofconvnetfeatures 110\u003c\/p\u003e \u003cp\u003e4.3.Anomalydetectioninimagetimeseries 113\u003c\/p\u003e \u003cp\u003e4.4.Functionalimagetimeseriesclustering 119\u003c\/p\u003e \u003cp\u003e4.5.Conclusion 123\u003c\/p\u003e \u003cp\u003e4.6.References 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFatima KARBOU, Guillaume JAMES, Philippe DURAND\u003c\/p\u003e \u003cp\u003eand Abdourrahmane M. ATTO\u003c\/p\u003e \u003cp\u003e5.1. Introduction 127\u003c\/p\u003e \u003cp\u003e5.2.Testareaanddata 129\u003c\/p\u003e \u003cp\u003e5.3.WetsnowdetectionusingSentinel-1 129\u003c\/p\u003e \u003cp\u003e5.4.Metricstodetectwetsnow 133\u003c\/p\u003e \u003cp\u003e5.5.Discussion 138\u003c\/p\u003e \u003cp\u003e5.6.Conclusion 143\u003c\/p\u003e \u003cp\u003e5.7.Acknowledgements 143\u003c\/p\u003e \u003cp\u003e5.8.References 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbdourrahmane M. ATTO, Aluísio PINHEIRO, Guillaume GINOLHAC\u003c\/p\u003e \u003cp\u003eand Pedro MORETTIN\u003c\/p\u003e \u003cp\u003e6.1. Introduction 145\u003c\/p\u003e \u003cp\u003e6.2. Random field model of a cyclone texture 148\u003c\/p\u003e \u003cp\u003e6.2.1.Cyclonetexturefeature 149\u003c\/p\u003e \u003cp\u003e6.2.2. Wavelet-based power spectral densities and cyclone fields 150\u003c\/p\u003e \u003cp\u003e6.2.3. Fractional spectral power decay model 153\u003c\/p\u003e \u003cp\u003e6.3.Cyclonefieldeyedetectionandtracking 157\u003c\/p\u003e \u003cp\u003e6.3.1.Cycloneeyedetection 157\u003c\/p\u003e \u003cp\u003e6.3.2.Dynamicfractalfieldeyetracking 158\u003c\/p\u003e \u003cp\u003e6.4. Cyclone field intensity evolution prediction 159\u003c\/p\u003e \u003cp\u003e6.5.Discussion 161\u003c\/p\u003e \u003cp\u003e6.6.Acknowledgements 163\u003c\/p\u003e \u003cp\u003e6.7.References 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMinh-Tan PHAM and Grégoire MERCIER\u003c\/p\u003e \u003cp\u003e7.1. Introduction 167\u003c\/p\u003e \u003cp\u003e7.2. Texture representation and characterization using local extrema 169\u003c\/p\u003e \u003cp\u003e7.2.1.Motivationandapproach 169\u003c\/p\u003e \u003cp\u003e7.2.2. Local extrema keypoints within SAR images 172\u003c\/p\u003e \u003cp\u003e7.3.Unsupervisedchangedetection 175\u003c\/p\u003e \u003cp\u003e7.3.1. Proposed framework 175\u003c\/p\u003e \u003cp\u003e7.3.2. Weighted graph construction from keypoints 176\u003c\/p\u003e \u003cp\u003e7.3.3.Changemeasure(CM)generation 178\u003c\/p\u003e \u003cp\u003e7.4.Experimentalstudy 179\u003c\/p\u003e \u003cp\u003e7.4.1. Data description and evaluation criteria 179\u003c\/p\u003e \u003cp\u003e7.4.2.Changedetectionresults 181\u003c\/p\u003e \u003cp\u003e7.4.3.Sensitivitytoparameters 185\u003c\/p\u003e \u003cp\u003e7.4.4.ComparisonwiththeNLMmodel 188\u003c\/p\u003e \u003cp\u003e7.4.5. Analysis of the algorithm complexity 191\u003c\/p\u003e \u003cp\u003e7.5.Applicationtoglacierflowmeasurement 192\u003c\/p\u003e \u003cp\u003e7.5.1. Proposed method 193\u003c\/p\u003e \u003cp\u003e7.5.2.Results 194\u003c\/p\u003e \u003cp\u003e7.6.Conclusion 196\u003c\/p\u003e \u003cp\u003e7.7.References 197\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Multitemporal Analysis of Sentinel-1\/2 Images for Land Use Monitoring at Regional Scale 201\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAndrea GARZELLI and Claudia ZOPPETTI\u003c\/p\u003e \u003cp\u003e8.1. Introduction 201\u003c\/p\u003e \u003cp\u003e8.2. Proposed method 203\u003c\/p\u003e \u003cp\u003e8.2.1.Testsiteanddata 206\u003c\/p\u003e \u003cp\u003e8.3.SARprocessing 209\u003c\/p\u003e \u003cp\u003e8.4.Opticalprocessing 215\u003c\/p\u003e \u003cp\u003e8.5.Combinationlayer 217\u003c\/p\u003e \u003cp\u003e8.6.Results 219\u003c\/p\u003e \u003cp\u003e8.7.Conclusion 220\u003c\/p\u003e \u003cp\u003e8.8.References 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Statistical Difference Models for Change Detection in Multispectral Images 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eMassimo ZANETTI, Francesca BOVOLO and Lorenzo BRUZZONE\u003c\/p\u003e \u003cp\u003e9.1. Introduction 223\u003c\/p\u003e \u003cp\u003e9.2. Overview of the change detection problem 225\u003c\/p\u003e \u003cp\u003e9.2.1. Change detection methods for multispectral images 227\u003c\/p\u003e \u003cp\u003e9.2.2. Challenges addressed in this chapter 230\u003c\/p\u003e \u003cp\u003e9.3 The Rayleigh–Rice mixture model for the magnitude of the differenceimage 231\u003c\/p\u003e \u003cp\u003e9.3.1. Magnitude image statistical mixture model 231\u003c\/p\u003e \u003cp\u003e9.3.2.Bayesiandecision 233\u003c\/p\u003e \u003cp\u003e9.3.3. Numerical approach to parameter estimation 234\u003c\/p\u003e \u003cp\u003e9.4. A compound multiclass statistical model of the difference image 239\u003c\/p\u003e \u003cp\u003e9.4.1. Difference image statistical mixture model 240\u003c\/p\u003e \u003cp\u003e9.4.2. Magnitude image statistical mixture model 245\u003c\/p\u003e \u003cp\u003e9.4.3.Bayesiandecision 248\u003c\/p\u003e \u003cp\u003e9.4.4. Numerical approach to parameter estimation 249\u003c\/p\u003e \u003cp\u003e9.5.Experimentalresults 253\u003c\/p\u003e \u003cp\u003e9.5.1.Datasetdescription 253\u003c\/p\u003e \u003cp\u003e9.5.2.Experimentalsetup 256\u003c\/p\u003e \u003cp\u003e9.5.3. Test 1: Two-class Rayleigh–Rice mixture model 256\u003c\/p\u003e \u003cp\u003e9.5.4. Test 2: Multiclass Rician mixture model 260\u003c\/p\u003e \u003cp\u003e9.6.Conclusion 266\u003c\/p\u003e \u003cp\u003e9.7.References 267\u003c\/p\u003e \u003cp\u003eList of Authors 275\u003c\/p\u003e \u003cp\u003eIndex 277\u003c\/p\u003e \u003cp\u003eSummary of Volume 2 281\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446813126936,"sku":"9781789450569","price":113.79,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781789450569.jpg?v=1785114496","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/change-detection-and-image-time-series-analysis-1-unervised-methods-hardback-9781789450569","provider":"Freshly Printed Books","version":"1.0","type":"link"}