| Description |
1 online resource : illustrations, maps |
| Series |
Series in remote sensing ; vol. 3 |
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Series in remote sensing ; vol. 3.
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| Contents |
Foreword; Contents; A Comparative Assessment of Similarity Measures for Registration of Multi-Temporal Remote Sensing Images H.-M. Chen, M.K. Arora, and P.K. Varshney; Image Analysis and Algorithms; 1. Introduction; 2. Computation of mutual information between two images; 3. Generalized partial volume estimation (GPVE) algorithm for joint histogram estimation; 4. Registration consistency; 5. Experimental results and discussion; 6. Conclusions; Acknowledgments; References |
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Attempts at Automatic Video Frame Mosaicing and Band to Band Registration of Data Generated by the Variable Interference Filter Imaging Spectrometer (VIFIS) N.E. Kirby, J.G.C. Monk, J.M. Anderson, and A.P. Cracknell1. Introduction; 2. Video Frame Registration; 2.1. Correlation based matching; 2.2. Ordinal Measures; 2.3. Invariant Moments; 2.4. False Match Removal; 3. Band to Band Registration; 4. Conclusion; References; Extending Time-Series of Satellite Images by Radiometric Intercalibration A. Roder, T. Kummerle, and J. Hill; 1. Introduction; 1.1. The importance of sensor calibration |
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1.2. Radiometric intercalibration1.3. The radiometric intercalibration approach; 2. Datasets; 3. Input data specifications and sensitivity analyses; 4. Radiometric intercalibration -- results and discussion; 5. Conclusions and future perspectives; Acknowledgments; References; Feature Detection in Multi-Temporal SAR Images F.T. Bujor, E. Trouve, L. Valet, Ph. Bolon, J.M. Nicolas, and J.P. Rudant; Abstract; 1. Introduction; 2. Change detection in multi-temporal SAR imagery; 3. Information extraction; 3.1. Spatial edge attribute; 3.2. Temporal change attribute; 3.3. 3D-Texture attribute |
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4. Symbolic information fusion5. Application; 6. Conclusions and perspectives; References; Trajectory of Dynamic Clusters in Image Time-Series P. Heas, M. Datcu, and A. Giros; 1. Introduction; 1.1. Times series of satellite images; 1.2. Information mining by analyzing the cluster dynamics; 2. Investigating the dynamics of clusters; 2.1. Multitempoml clustering; 2.2. Time- localized clustering; 2.3. Analyzes of the dynamics of the feature space; 2.4. Proposal of solutions for dynamic cluster modeling; 2.4.1. Minimum description length (MDL) principle for Gaussian mixture modeling |
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2.4.2. Modeling a Gaussian mixture evolution3. Results; 4. Conclusion; References; What Have Quantitative Change Indicators and Fractal Dimension in Common? K. Nackaerts, S. Fleck, B. Muys, and P. Coppin; 1. Introduction; 2. Materials and methods; 2.1. Study area; 2.2. Field measurements of leaf urea index and fractal dimension; 2.3. Satellite image analysis; 3. Results and discussion; 3.1. Field measurements; 4. Conclusions; Acknowledgements; References; A Reduced Rank Regression Mixture Model for Change Validation in Aerial Images F. Pe'rez Nava and J.M. Ga'lvez Lamolda; 1. Introduction |
| Summary |
The development of effective methodologies for the analysis of multi-temporal data is one of the most important and challenging issues that the remote sensing community will face in the coming years. Its importance and timeliness are directly related to the ever-increasing quantity of multi-temporal data provided by the numerous remote sensing satellites that orbit our planet. The synergistic use of multi-temporal remote sensing data and advanced analysis methodologies results in the possibility of solving complex problems related to the monitoring of the Earth's surface and atmosphere at diff |
| Analysis |
Multi temporal remote sensing images |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Remote sensing -- Data processing -- Congresses
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TECHNOLOGY & ENGINEERING -- Remote Sensing & Geographic Information Systems.
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Remote sensing -- Data processing
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| Genre/Form |
Conference papers and proceedings
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| Form |
Electronic book
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| Author |
Smits, Paul.
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Bruzzone, Lorenzo.
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| ISBN |
9789812702630 |
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9812702636 |
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