| Description |
1 online resource (288 p.) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Notes |
Description based upon print version of record |
|
4.4. Deep learning-based time series analysis approaches |
| Form |
Electronic book
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| Author |
Bovolo, Francesca
|
|
Bruzzone, Lorenzo
|
| ISBN |
9781119882275 |
|
1119882273 |
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