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Change detection and image time-series analysis. 1, Unsupervised methods

Atto, Abdourrahmane M.

Change detection and image time-series analysis. 1, Unsupervised methods

Atto, Abdourrahmane M.

9781119882268

9781119882244

London, UK : ISTE, Ltd. ; Hoboken, NJ : Wiley, 2022.

1 online resource (304 pages)

Image. Remote sensing imagery

Chapter 5. Thresholds and Distances to Better Detect Wet Snow over Mountains with Sentinel-1 Image Time Series.

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

Change 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. Chapter 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.

John Wiley and Sons

Time-series analysis.

Série chronologique.

Time-series analysis

Atto, Abdourrahmane M.

Bovolo, Francesca.

Bruzzone, Lorenzo.

https://onlinelibrary.wiley.com/doi/book/10.1002/9781119882268