Change Detection and Image Time Series Analysis 2

Supervised 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 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series.Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches.Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns.Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations,Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Produktinformation

Utgivningsdatum2022-01-04
Mått10 x 10 x 10 mm
Vikt454 g
FormatInbunden
SpråkEngelska
Antal sidor272
FörlagISTE Ltd
ISBN9781789450576

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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 ixAbdourrahmane M. ATTO, Francesca BOVOLO and Lorenzo BRUZZONEList of NotationsChapter 1 Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series 1Ihsen HEDHLI, Gabriele MOSER, Sebastiano B. SERPICOand Josiane ZERUBIA1.1. Introduction 11.1.1. The role of multisensor data in time series classification 11.1.2. Multisensor and multiresolution classification 21.1.3.Previouswork 51.2. Methodology 91.2.1. Overview of the proposed approaches 91.2.2. Hierarchical model associated with the first proposed method 101.2.3. Hierarchical model associated with the second proposed method 131.2.4. Multisensor hierarchical MPM inference 141.2.5. Probability density estimation through finite mixtures 171.3.Examplesofexperimentalresults 191.3.1.Resultsofthefirstmethod 191.3.2.Resultsofthesecondmethod 221.4.Conclusion 26xiii1.5.Acknowledgments 261.6.References 27Chapter 2 Pixel-based Classification Techniques for Satellite Image Time Series 33Charlotte PELLETIER and Silvia VALERO2.1. Introduction 332.2. Basic concepts in supervised remote sensing classification 352.2.1. Preparing data before it is fed into classification algorithms 352.2.2. Key considerations when training supervised classifiers 392.2.3. Performance evaluation of supervised classifiers 412.3.Traditionalclassificationalgorithms 452.3.1. Support vector machines 452.3.2. Random forests 512.3.3. k-nearest neighbor 562.4. Classification strategies based on temporal feature representations 592.4.1. Phenology-based classification approaches 602.4.2 Dictionary-based classificationapproaches 612.4.3 Shapelet-based classificationapproaches 622.5.Deeplearningapproaches 632.5.1. Introduction to deep learning 642.5.2.Convolutionalneuralnetworks 682.5.3.Recurrentneuralnetworks 712.6.References 75Chapter 3 Semantic Analysis of Satellite Image Time Series 85Corneliu Octavian DUMITRU and Mihai DATCU3.1. Introduction 853.1.1.TypicalSITSexamples 893.1.2. Irregular acquisitions 903.1.3.Thechapterstructure 963.2.WhyaresemanticsneededinSITS? 963.3.Similaritymetrics 973.4. Feature methods 983.5. Classification methods 983.5.1.Activelearning 993.5.2.Relevancefeedback 1003.5.3. Compression-based pattern recognition 1003.5.4.LatentDirichletallocation 1013.6.Conclusion 102vii3.7.Acknowledgments 1053.8.References 105Chapter 4 Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond 109Matthieu MOLINIER, Jukka MIETTINEN,DinoIENCO,ShiQIU and Zhe ZHU4.1. Introduction 1094.2. Annual time series 1114.2.1. Overview of annual time series methods 1114.2.2 Examples of annual times series analysis applications for environmentalmonitoring 1124.2.3.Towardsdensetimeseriesanalysis 1164.3. Dense time series analysis using all available data 1174.3.1. Making dense time series consistent 1184.3.2. Change detection methods 1214.3.3.Summaryandfuturedevelopments 1254.4. Deep learning-based time series analysis approaches 1264.4.1 Recurrent Neural Network (RNN) for Satellite Image TimeSeries 1294.4.2 Convolutional Neural Networks (CNN) for Satellite Image TimeSeries 1314.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series 1344.4.4. Synthesis and future developments 1364.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches 1364.5.1 Increased image acquisition frequency: from time series to spacebornetime-lapseandvideos 1374.5.2. Deep learning and computer vision as technology enablers 1384.5.3.Futuresteps 1394.6.References 140Chapter 5 A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images 155Gülşen TAŞKIN, EsraERTEN and Enes Oğuzhan ALATAŞ5.1. Introduction 1555.1.1. Research methodology and statistics 1595.2. Satellite-based earthquake damage assessment 1655.3. Pre-processing of satellite images before damage assessment 1675.4. Multi-source image analysis 1685.5. Contextual feature mining for damage assessment 1695.5.1.Texturalfeatures 1705.5.2. Filter-based methods 1735.6. Multi-temporal image analysis for damage assessment 1755.6.1. Use of machine learning in damage assessment problem 1765.6.2. Rapid earthquake damage assessment 1805.7. Understanding damage following an earthquake using satellite-based SAR 1815.7.1. SAR fundamental parameters and acquisition vector 1855.7.2. Coherent methods for damage assessment 1885.7.3. Incoherent methods for damage assessment 1925.7.4. Post-earthquake-only SAR data-based damage assessment 1955.7.5 Combination of coherent and incoherent methods for damage assessment 1965.7.6.Summary 1985.8. Use of auxiliary data sources 2005.9.Damagegrades 2005.10.Conclusionanddiscussion 2035.11.References 205Chapter 6 Multiclass Multilabel Change of State Transfer Learning from Image Time Series 223Abdourrahmane M. ATTO,HélaHADHRI, FlavienVERNIERand Emmanuel TROUVÉ6.1. Introduction 2236.2. Coarse- to fine-grained change of state dataset 2256.3. Deep transfer learning models for change of state classification 2326.3.1.Deeplearningmodellibrary 2326.3.2.GraphstructuresfortheCNNlibrary 2346.3.3. Dimensionalities of the learnables for the CNN library 2366.4.Changeofstateanalysis 2376.4.1 Transfer learning adaptations for the change of state classificationissues 2386.4.2.Experimentalresults 2396.5.Conclusion 2436.6.Acknowledgments 2446.7.References 244List of Authors 247Index 249Summary of Volume 1 253