Change detection and image time series analysis 2 : supervised methods / / edited by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021]; ©2021
• Descrizione fisica: 1 online resource (288 pages)
• Disciplina: 621.367
• Soggetto topico: Image analysis
• Soggetto genere / forma: Electronic books.
• Persona (resp. second.): BruzzoneLorenzo; BovoloFrancesca; AttoAbdourrahmane M.
• Nota di contenuto: Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Notations -- 1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series -- 1.1. Introduction -- 1.1.1. The role of multisensor data in time series classification -- 1.1.2. Multisensor and multiresolution classification -- 1.1.3. Previous work -- 1.2. Methodology -- 1.2.1. Overview of the proposed approaches -- 1.2.2. Hierarchical model associated with the first proposed method -- 1.2.3. Hierarchical model associated with the second proposed method -- 1.2.4. Multisensor hierarchical MPM inference -- 1.2.5. Probability density estimation through -- 1.3. Examples of experimental results -- 1.3.1. Results of the first method -- 1.3.2. Results of the second method -- 1.4. Conclusion -- 1.5. Acknowledgments -- 1.6. References -- 2. Pixel-based Classification Techniques for Satellite Image Time Series -- 2.1. Introduction -- 2.2. Basic concepts in supervised remote sensing classification -- 2.2.1. Preparing data before it is fed into classification algorithms -- 2.2.2. Key considerations when training supervised classifiers -- 2.2.3. Performance evaluation of supervised classifiers -- 2.3. Traditional classification algorithms -- 2.3.1. Support vector machines -- 2.3.2. Random forests -- 2.3.3. k-nearest neighbor -- 2.4. Classification strategies based on temporal feature representations -- 2.4.1. Phenology-based classification approaches -- 2.4.2. Dictionary-based classification approaches -- 2.4.3. Shapelet-based classification approaches -- 2.5. Deep learning approaches -- 2.5.1. Introduction to deep learning -- 2.5.2. Convolutional neural networks -- 2.5.3. Recurrent neural networks -- 2.6. References -- 3. Semantic Analysis of Satellite Image Time Series -- 3.1. Introduction. ; 3.1.1. Typical SITS examples -- 3.1.2. Irregular acquisitions -- 3.1.3. The chapter structure -- 3.2. Why are semantics needed in SITS? -- 3.3. Similarity metrics -- 3.4. Feature methods -- 3.5. Classification methods -- 3.5.1. Active learning -- 3.5.2. Relevance feedback -- 3.5.3. Compression-based pattern recognition -- 3.5.4. Latent Dirichlet allocation -- 3.6. Conclusion -- 3.7. Acknowledgments -- 3.8. References -- 4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond -- 4.1. Introduction -- 4.2. Annual time series -- 4.2.1. Overview of annual time series methods -- 4.2.2. Examples of annual times series analysis applications for environmental monitoring -- 4.2.3. Towards dense time series analysis -- 4.3. Dense time series analysis using all available data -- 4.3.1. Making dense time series consistent -- 4.3.2. Change detection methods -- 4.3.3. Summary and future developments -- 4.4. Deep learning-based time series analysis approaches -- 4.4.1. Recurrent Neural Network (RNN) for Satellite Image Time Series -- 4.4.2. Convolutional Neural Networks (CNN) for Satellite Image Time Series -- 4.4.3. Hybrid models: Convolutional Recurrent Neural Network (ConvRNN) models for Satellite Image Time Series -- 4.4.4. Synthesis and future developments -- 4.5. Beyond satellite image time series and deep learning: convergence between time series and video approaches -- 4.5.1. Increased image acquisition frequency: from time series to spaceborne time-lapse and videos -- 4.5.2. Deep learning and computer vision as technology enablers -- 4.5.3. Future steps -- 4.6. References -- 5. A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images -- 5.1. Introduction -- 5.1.1. Research methodology and statistics -- 5.2. Satellite-based earthquake damage assessment. ; 5.3. Pre-processing of satellite images before damage assessment -- 5.4. Multi-source image analysis -- 5.5. Contextual feature mining for damage assessment -- 5.5.1. Textural features -- 5.5.2. Filter-based methods -- 5.6. Multi-temporal image analysis for damage assessment -- 5.6.1. Use of machine learning in damage assessment problem -- 5.6.2. Rapid earthquake damage assessment -- 5.7. Understandingdamage followingan earthquakeusing satellite-based SAR -- 5.7.1. SAR fundamental parameters and acquisition vector -- 5.7.2. Coherent methods for damage assessment -- 5.7.3. Incoherent methods for damage assessment -- 5.7.4. Post-earthquake-only SAR data-based damage assessment -- 5.7.5. Combination of coherent and incoherent methods for damage assessment -- 5.7.6. Summary -- 5.8. Use of auxiliary data sources -- 5.9. Damage grades -- 5.10. Conclusion and discussion -- 5.11. References -- 6. Multiclass Multilabel Change of State Transfer Learning from Image Time Series -- 6.1. Introduction -- 6.2. Coarse- to fine-grained change of state dataset -- 6.3. Deep transfer learning models for change of state classification -- 6.3.1. Deep learning model library -- 6.3.2. Graph structures for the CNN library -- 6.3.3. Dimensionalities of the learnables for the CNN library -- 6.4. Change of state analysis -- 6.4.1. Transfer learning adaptations for the change of state classification issues -- 6.4.2. Experimental results -- 6.5. Conclusion -- 6.6. Acknowledgments -- 6.7. References -- List of Authors -- Index -- Summary of Volume 1 -- EULA.
• Titolo autorizzato: Change detection and image time series analysis 2
• ISBN: 1-119-88228-1; 1-119-88229-X; 1-119-88227-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910554872203321
• Lo trovi qui: Univ. Federico II