Change Detection and Image Time Series Analysis 1 Unsupervised Methods 1st Edition by Abdourrahmane Atto 1119882251 9781119882251

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ISBN-10 :  1119882251 

ISBN-13 :  9781119882251

Author :  Abdourrahmane M. Atto 

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.

Change Detection and Image Time Series Analysis 1: Unsupervised Methods 1st Table of contents:

Volume 1: Unsupervised methods
Volume 2: Supervised methods
List of Notations
1 Unsupervised Change Detection in Multitemporal Remote Sensing Images
1.1. Introduction
1.2. Unsupervised change detection in multispectral images
1.3. Unsupervised multiclass change detection approaches based on modeling spectral–spatial information
1.4. Dataset description and experimental setup
1.5. Results and discussion
1.6. Conclusion
1.7. Acknowledgements
1.8. References
2 Change Detection in Time Series of Polarimetric SAR Images
2.1. Introduction
2.2. Test theory and matrix ordering
2.3. The basic change detection algorithm
2.4. Applications
2.5. References
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
3.4. Dissimilarity measures
3.5. Change detection based on structured covariances
3.6. Conclusion
3.7. References
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
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
6 Fractional Field Image Time Series Modeling and Application to Cyclone Tracking
6.1. Introduction
6.2. Random field model of a cyclone texture
6.3. Cyclone field eye detection and tracking
6.4. Cyclone field intensity evolution prediction
6.5. Discussion
6.6. Acknowledgements
6.7. References
7 Graph of Characteristic Points for Texture Tracking: Application to Change Detection and Glacier Flow Measurement from SAR Images
7.1. Introduction
7.2. Texture representation and characterization using local extrema
7.3. Unsupervised change detection
7.4. Experimental study
7.5. Application to glacier flow measurement
7.6. Conclusion
7.7. References
8 Multitemporal Analysis of Sentinel-1/2 Images for Land Use Monitoring at Regional Scale
8.1. Introduction
8.2. Proposed method
8.3. SAR processing
8.4. Optical processing
8.5. Combination layer
8.6. Results
8.7. Conclusion
8.8. References
9 Statistical Difference Models for Change Detection in Multispectral Images
9.1. Introduction
9.2. Overview of the change detection problem
9.3. The Rayleigh–Rice mixture model for the magnitude of the difference image
9.4. A compound multiclass statistical model of the difference image
9.5. Experimental results
9.6. Conclusion
9.7. References

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