Change detection and image time-series analysis. 1 : unsupervised methods

Change detection and image time-series analysis 1 : unsupervised methods 1 :

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 mul...

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Detalles Bibliográficos
Otros Autores: Atto, Abdourrahmane M., author (author), Bovolo, Francesca, author, Bruzzone, Lorenzo, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc [2022]
Edición:1st edition
Materias:
Time-series analysis.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009724229906719
Tabla de Contenidos:
  • 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.

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