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Change detection and image time series analysis . 2 : supervised methods / / coordinated by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone
| Change detection and image time series analysis . 2 : supervised methods / / coordinated by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated |
| Descrizione fisica | 1 online resource (288 pages) : illustrations (chiefly colour) |
| Disciplina | 621.367 |
| Soggetto topico | Image analysis |
| ISBN |
9781119882282
1119882281 1-119-88229-X 1-119-88227-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Hierarchical Markov Random Fields for High Resolution Land Cover Classification of Multisensor and Multiresolution Image Time Series / Ihsen Hedhli, Gabriele Moser, Sebastiano B. Serpico and Josiane Zerubia -- ; 2. Pixel-based Classification Techniques for Satellite Image Time Series / Charlotte Pelletier and Silvia Valero -- ; 3. Semantic Analysis of Satellite Image Time Series / Corneliu Octavian Dumitru and Mihai Datcu -- ; 4. Optical Satellite Image Time Series Analysis for Environment Applications: From Classical Methods to Deep Learning and Beyond / Matthieu Molinier, Jukka Miettinen, Dino Ienco, Shi Qiu and Zhe Zhu -- ; 5. A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images / Gülşen Taşkin, Esra Erten and Enes Oğuzhan Alataş -- ; 6. Multiclass Multilabel Change of State Transfer Learning from Image Time Series / Abdourrahmane M. Atto, Héla Hadhri, Flavien Vernier and Emmanuel Trouvé. |
| Record Nr. | UNINA-9910829874203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Change detection and image time series analysis 2 : supervised methods / / edited by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone
| Change detection and image time series analysis 2 : supervised methods / / edited by Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] |
| Descrizione fisica | 1 online resource (288 pages) |
| Disciplina | 621.367 |
| Soggetto topico | Image analysis |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-88228-1
1-119-88229-X 1-119-88227-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNINA-9910554872203321 |
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone
| Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone |
| Autore | Atto Abdourrahmane M. |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (304 pages) |
| Disciplina | 519.55 |
| Soggetto topico |
Time-series analysis
Time-series analysis - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-88225-7
1-119-88226-5 1-119-88224-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Notations -- Chapter 1. Unsupervised Change Detection in Multitemporal Remote Sensing Images -- 1.1. Introduction -- 1.2. Unsupervised change detection in multispectral images -- 1.2.1. Related concepts -- 1.2.2. Open issues and challenges -- 1.2.3. Spectral-spatial unsupervised CD techniques -- 1.3. Unsupervised multiclass change detection approaches based on modeling spectral-spatial information -- 1.3.1. Sequential spectral change vector analysis (S2CVA) -- 1.3.2. Multiscale morphological compressed change vector analysis -- 1.3.3. Superpixel-level compressed change vector analysis -- 1.4. Dataset description and experimental setup -- 1.4.1. Dataset description -- 1.4.2. Experimental setup -- 1.5. Results and discussion -- 1.5.1. Results on the Xuzhou dataset -- 1.5.2. Results on the Indonesia tsunami dataset -- 1.6. Conclusion -- 1.7. Acknowledgements -- 1.8. References -- Chapter 2. Change Detection inTime Series of Polarimetric SAR Images -- 2.1. Introduction -- 2.1.1. The problem -- 2.1.2. Important concepts illustrated bymeans of the gamma distribution -- 2.2. Test theory and matrix ordering -- 2.2.1. Test for equality of two complex Wishart distributions -- 2.2.2. Test for equality of k-complex Wishart distributions -- 2.2.3. The block diagonal case -- 2.2.4. The Loewner order -- 2.3. The basic change detection algorithm -- 2.4. Applications -- 2.4.1. Visualizing changes -- 2.4.2. Fieldwise change detection -- 2.4.3. Directional changes using the Loewner ordering -- 2.4.4. Software availability -- 2.5. References -- Chapter 3. An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series -- 3.1. Introduction -- 3.2. Dataset description -- 3.3. Statistical modeling of SAR images -- 3.3.1. The data.
3.3.2. Gaussian model -- 3.3.3. Non-Gaussian modeling -- 3.4. Dissimilarity measures -- 3.4.1. Problem formulation -- 3.4.2. Hypothesis testing statistics -- 3.4.3. Information-theoretic measures -- 3.4.4. Riemannian geometry distances -- 3.4.5. Optimal transport -- 3.4.6. Summary -- 3.4.7. Results of change detectors on the UAVSAR dataset -- 3.5. Change detection based on structured covariances -- 3.5.1. Low-rank Gaussian change detector -- 3.5.2. Low-rank compound Gaussian change detector -- 3.5.3. Results of low-rank change detectors on the UAVSAR dataset -- 3.6. Conclusion -- 3.7. References -- Chapter 4. Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy -- 4.1. Introduction -- 4.2. Parametric modeling of convnet features -- 4.3. Anomaly detection in image time series -- 4.4. Functional image time series clustering -- 4.5. Conclusion -- 4.6. References -- Chapter 5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series -- 5.1. Introduction -- 5.2. Test area and data -- 5.3. Wet snow detection using Sentinel-1 -- 5.4. Metrics to detect wet snow -- 5.5. Discussion -- 5.6. Conclusion -- 5.7. Acknowledgements -- 5.8. References -- Chapter 6. Fractional Field Image Time Series Modeling and Application to Cyclone Tracking -- 6.1. Introduction -- 6.2. Random field model of a cyclone texture -- 6.2.1. Cyclone texture feature -- 6.2.2. Wavelet-based power spectral densities and cyclone -- 6.2.3. Fractional spectral power decay model -- 6.3. Cyclone field eye detection and tracking -- 6.3.1. Cyclone eye detection -- 6.3.2. Dynamic fractal field eye tracking -- 6.4. Cyclone field intensity evolution prediction -- 6.5. Discussion -- 6.6. Acknowledgements -- 6.7. References. Chapter 7. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Image -- 7.1. Introduction -- 7.2. Texture representation and characterization using local extrema -- 7.2.1. Motivation and approach -- 7.2.2. Local extrema keypoints within SAR images -- 7.3. Unsupervised change detection -- 7.3.1. Proposed framework -- 7.3.2. Weighted graph construction from keypoints -- 7.3.3. Change measure (CM) generation -- 7.4. Experimental study -- 7.4.1. Data description and evaluation criteria -- 7.4.2. Change detection results -- 7.4.3. Sensitivity to parameters -- 7.4.4. Comparison with the NLM model -- 7.4.5. Analysis of the algorithm complexity -- 7.5. Application to glacier flow measurement -- 7.5.1. Proposed method -- 7.5.2. Results -- 7.6. Conclusion -- 7.7. References -- Chapter 8. Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale -- 8.1. Introduction -- 8.2. Proposed method -- 8.2.1. Test site and data -- 8.3. SAR processing -- 8.4. Optical processing -- 8.5. Combination layer -- 8.6. Results -- 8.7. Conclusion -- 8.8. References -- Chapter 9. Statistical Difference Models for Change Detection in Multispectral Images -- 9.1. Introduction -- 9.2. Overview of the change detection problem -- 9.2.1. Change detection methods for multispectral images -- 9.2.2. Challenges addressed in this chapter -- 9.3. The Rayleigh-Rice mixture model for the magnitude of the difference image -- 9.3.1. Magnitude image statistical mixture model -- 9.3.2. Bayesian decision -- 9.3.3. Numerical approach to parameter estimation -- 9.4. A compound multiclass statistical model of the difference image -- 9.4.1. Difference image statistical mixture model -- 9.4.2. Magnitude image statistical mixture model -- 9.4.3. Bayesian decision. 9.4.4. Numerical approach to parameter estimation -- 9.5. Experimental results -- 9.5.1. Dataset description -- 9.5.2. Experimental setup -- 9.5.3. Test 1: Two-class Rayleigh-Rice mixture model -- 9.5.4. Test 2: Multiclass Rician mixture model -- 9.6. Conclusion -- 9.7. References -- List of Authors -- Index -- Summary of Volume 2 -- EULA. |
| Record Nr. | UNINA-9910554803303321 |
Atto Abdourrahmane M.
|
||
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone
| Change detection and image time-series analysis . 1 : unsupervised methods / / Abdourrahmane M. Atto, Francesca Bovolo, Lorenzo Bruzzone |
| Autore | Atto Abdourrahmane M. |
| Edizione | [1st edition.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] |
| Descrizione fisica | 1 online resource (304 pages) |
| Disciplina | 519.55 |
| Soggetto topico | Time-series analysis |
| ISBN |
1-119-88225-7
1-119-88226-5 1-119-88224-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Notations -- Chapter 1. Unsupervised Change Detection in Multitemporal Remote Sensing Images -- 1.1. Introduction -- 1.2. Unsupervised change detection in multispectral images -- 1.2.1. Related concepts -- 1.2.2. Open issues and challenges -- 1.2.3. Spectral-spatial unsupervised CD techniques -- 1.3. Unsupervised multiclass change detection approaches based on modeling spectral-spatial information -- 1.3.1. Sequential spectral change vector analysis (S2CVA) -- 1.3.2. Multiscale morphological compressed change vector analysis -- 1.3.3. Superpixel-level compressed change vector analysis -- 1.4. Dataset description and experimental setup -- 1.4.1. Dataset description -- 1.4.2. Experimental setup -- 1.5. Results and discussion -- 1.5.1. Results on the Xuzhou dataset -- 1.5.2. Results on the Indonesia tsunami dataset -- 1.6. Conclusion -- 1.7. Acknowledgements -- 1.8. References -- Chapter 2. Change Detection inTime Series of Polarimetric SAR Images -- 2.1. Introduction -- 2.1.1. The problem -- 2.1.2. Important concepts illustrated bymeans of the gamma distribution -- 2.2. Test theory and matrix ordering -- 2.2.1. Test for equality of two complex Wishart distributions -- 2.2.2. Test for equality of k-complex Wishart distributions -- 2.2.3. The block diagonal case -- 2.2.4. The Loewner order -- 2.3. The basic change detection algorithm -- 2.4. Applications -- 2.4.1. Visualizing changes -- 2.4.2. Fieldwise change detection -- 2.4.3. Directional changes using the Loewner ordering -- 2.4.4. Software availability -- 2.5. References -- Chapter 3. An Overview of Covariance-based Change Detection Methodologies in Multivariate SAR Image Time Series -- 3.1. Introduction -- 3.2. Dataset description -- 3.3. Statistical modeling of SAR images -- 3.3.1. The data.
3.3.2. Gaussian model -- 3.3.3. Non-Gaussian modeling -- 3.4. Dissimilarity measures -- 3.4.1. Problem formulation -- 3.4.2. Hypothesis testing statistics -- 3.4.3. Information-theoretic measures -- 3.4.4. Riemannian geometry distances -- 3.4.5. Optimal transport -- 3.4.6. Summary -- 3.4.7. Results of change detectors on the UAVSAR dataset -- 3.5. Change detection based on structured covariances -- 3.5.1. Low-rank Gaussian change detector -- 3.5.2. Low-rank compound Gaussian change detector -- 3.5.3. Results of low-rank change detectors on the UAVSAR dataset -- 3.6. Conclusion -- 3.7. References -- Chapter 4. Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy -- 4.1. Introduction -- 4.2. Parametric modeling of convnet features -- 4.3. Anomaly detection in image time series -- 4.4. Functional image time series clustering -- 4.5. Conclusion -- 4.6. References -- Chapter 5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series -- 5.1. Introduction -- 5.2. Test area and data -- 5.3. Wet snow detection using Sentinel-1 -- 5.4. Metrics to detect wet snow -- 5.5. Discussion -- 5.6. Conclusion -- 5.7. Acknowledgements -- 5.8. References -- Chapter 6. Fractional Field Image Time Series Modeling and Application to Cyclone Tracking -- 6.1. Introduction -- 6.2. Random field model of a cyclone texture -- 6.2.1. Cyclone texture feature -- 6.2.2. Wavelet-based power spectral densities and cyclone -- 6.2.3. Fractional spectral power decay model -- 6.3. Cyclone field eye detection and tracking -- 6.3.1. Cyclone eye detection -- 6.3.2. Dynamic fractal field eye tracking -- 6.4. Cyclone field intensity evolution prediction -- 6.5. Discussion -- 6.6. Acknowledgements -- 6.7. References. Chapter 7. Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Image -- 7.1. Introduction -- 7.2. Texture representation and characterization using local extrema -- 7.2.1. Motivation and approach -- 7.2.2. Local extrema keypoints within SAR images -- 7.3. Unsupervised change detection -- 7.3.1. Proposed framework -- 7.3.2. Weighted graph construction from keypoints -- 7.3.3. Change measure (CM) generation -- 7.4. Experimental study -- 7.4.1. Data description and evaluation criteria -- 7.4.2. Change detection results -- 7.4.3. Sensitivity to parameters -- 7.4.4. Comparison with the NLM model -- 7.4.5. Analysis of the algorithm complexity -- 7.5. Application to glacier flow measurement -- 7.5.1. Proposed method -- 7.5.2. Results -- 7.6. Conclusion -- 7.7. References -- Chapter 8. Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale -- 8.1. Introduction -- 8.2. Proposed method -- 8.2.1. Test site and data -- 8.3. SAR processing -- 8.4. Optical processing -- 8.5. Combination layer -- 8.6. Results -- 8.7. Conclusion -- 8.8. References -- Chapter 9. Statistical Difference Models for Change Detection in Multispectral Images -- 9.1. Introduction -- 9.2. Overview of the change detection problem -- 9.2.1. Change detection methods for multispectral images -- 9.2.2. Challenges addressed in this chapter -- 9.3. The Rayleigh-Rice mixture model for the magnitude of the difference image -- 9.3.1. Magnitude image statistical mixture model -- 9.3.2. Bayesian decision -- 9.3.3. Numerical approach to parameter estimation -- 9.4. A compound multiclass statistical model of the difference image -- 9.4.1. Difference image statistical mixture model -- 9.4.2. Magnitude image statistical mixture model -- 9.4.3. Bayesian decision. 9.4.4. Numerical approach to parameter estimation -- 9.5. Experimental results -- 9.5.1. Dataset description -- 9.5.2. Experimental setup -- 9.5.3. Test 1: Two-class Rayleigh-Rice mixture model -- 9.5.4. Test 2: Multiclass Rician mixture model -- 9.6. Conclusion -- 9.7. References -- List of Authors -- Index -- Summary of Volume 2 -- EULA. |
| Record Nr. | UNINA-9910830500503321 |
|
Atto Abdourrahmane M.
|
||
| Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Kernel methods for remote sensing data analysis [[electronic resource] /] / edited by Gustavo Camps-Valls, Lorenzo Bruzzone
| Kernel methods for remote sensing data analysis [[electronic resource] /] / edited by Gustavo Camps-Valls, Lorenzo Bruzzone |
| Autore | Camps-Valls Gustavo <1972-> |
| Pubbl/distr/stampa | Chichester, West Sussex ; ; Hoboken, NJ, : Wiley, 2009 |
| Descrizione fisica | 1 online resource (444 p.) |
| Disciplina |
621.36/780285631
621.36780285631 |
| Altri autori (Persone) | BruzzoneLorenzo |
| Soggetto topico |
Remote sensing
Geography |
| ISBN |
1-282-29170-X
9786612291708 0-470-74899-0 0-470-74900-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Kernel Methods for Remote Sensing Data Analysis; Contents; About the editors; List of authors; Preface; Acknowledgments; List of symbols; List of abbreviations; I Introduction; 1 Machine learning techniques in remote sensing data analysis; 1.1 Introduction; 1.1.1 Challenges in remote sensing; 1.1.2 General concepts of machine learning; 1.1.3 Paradigms in remote sensing; 1.2 Supervised classification: algorithms and applications; 1.2.1 Bayesian classification strategy; 1.2.2 Neural networks; 1.2.3 Support Vector Machines (SVM); 1.2.4 Use of multiple classifiers; 1.3 Conclusion; Acknowledgments
References2 An introduction to kernel learning algorithms; 2.1 Introduction; 2.2 Kernels; 2.2.1 Measuring similarity with kernels; 2.2.2 Positive definite kernels; 2.2.3 Constructing the reproducing kernel Hilbert space; 2.2.4 Operations in RKHS; 2.2.5 Kernel construction; 2.2.6 Examples of kernels; 2.3 The representer theorem; 2.4 Learning with kernels; 2.4.1 Support vector classification; 2.4.2 Support vector regression; 2.4.3 Gaussian processes; 2.4.4 Multiple kernel learning; 2.4.5 Structured prediction using kernels; 2.4.6 Kernel principal component analysis 2.4.7 Applications of support vector algorithms2.4.8 Available software; 2.5 Conclusion; References; II Supervised image classification; 3 The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data; 3.1 Introduction; 3.2 Aspects of hyperspectral data and its acquisition; 3.3 Hyperspectral remote sensing and supervised classification; 3.4 Mathematical foundations of supervised classification; 3.4.1 Empirical risk minimization; 3.4.2 General bounds for a new risk minimization principle; 3.4.3 Structural risk minimization 3.5 From structural risk minimization to a support vector machine algorithm3.5.1 SRM for hyperplane binary classifiers; 3.5.2 SVM algorithm; 3.5.3 Kernel method; 3.5.4 Hyperparameters; 3.5.5 A toy example; 3.5.6 Multi-class classifiers; 3.5.7 Data centring; 3.6 Benchmark hyperspectral data sets; 3.6.1 The 4 class subset scene; 3.6.2 The 16 class scene; 3.6.3 The 9 class scene; 3.7 Results; 3.7.1 SVM implementation; 3.7.2 Effect of hyperparameter d; 3.7.3 Measure of accuracy of results; 3.7.4 Classifier results for the 4 class subset scene and the 16 class full scene 3.7.5 Results for the 9 class scene and comparison of SVM with other classifiers3.7.6 Effect of training set size; 3.7.7 Effect of simulated noisy data; 3.8 Using spatial coherence; 3.9 Why do SVMs perform better than other methods?; 3.10 Conclusions; References; 4 On training and evaluation of SVM for remote sensing applications; 4.1 Introduction; 4.2 Classification for thematic mapping; 4.3 Overview of classification by a SVM; 4.4 Training stage; 4.4.1 General recommendations on sample size; 4.4.2 Training a SVM; 4.4.3 Summary on training; 4.5 Testing stage; 4.5.1 General issues in testing 4.5.2 Specific issues for SVM classification |
| Record Nr. | UNINA-9910139774103321 |
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Camps-Valls Gustavo <1972->
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| Chichester, West Sussex ; ; Hoboken, NJ, : Wiley, 2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||