Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images :: Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 /
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...
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| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
[River Edge] N.J. :
World Scientific,
©2004.
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| Schriftenreihe: | Series in remote sensing ;
vol. 3. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource : illustrations, maps |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789812702630 9812702636 |
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| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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. | ||
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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 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 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 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 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 |
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| series | Series in remote sensing ; |
| series2 | Series in remote sensing ; |
| spelling | International Workshop on the Analysis of Multi-temporal Remote Sensing Images (2nd : 2003 : Ispra, Italy) Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / editors, Paul C. Smits, Lorenzo Bruzzone. Analysis of Multi-Temporal Remote Sensing Images Multitemp 2003 [River Edge] N.J. : World Scientific, ©2004. 1 online resource : illustrations, maps text txt rdacontent computer c rdamedia online resource cr rdacarrier Series in remote sensing ; vol. 3 Includes bibliographical references and index. Print version record. 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 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 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 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 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 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. Remote sensing Data processing Congresses. Télédétection Informatique Congrès. TECHNOLOGY & ENGINEERING Remote Sensing & Geographic Information Systems. bisacsh Remote sensing Data processing fast Multi temporal remote sensing images Conference papers and proceedings fast Smits, Paul. http://id.loc.gov/authorities/names/no2002114035 Bruzzone, Lorenzo. http://id.loc.gov/authorities/names/no2002114033 has work: Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (Text) https://id.oclc.org/worldcat/entity/E39PCFtFFHCPYXWD9WTTJPfBrm https://id.oclc.org/worldcat/ontology/hasWork Print version: International Workshop on the Analysis of Multi-Temporal Remote Sensing Images (2nd : 2003 : Ispra, Italy). Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. [River Edge] N.J. : World Scientific, ©2004 9789812389152 (OCoLC)57372099 Series in remote sensing ; vol. 3. http://id.loc.gov/authorities/names/n96041162 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514745 Volltext |
| spellingShingle | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / Series in remote sensing ; 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 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 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 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 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 Remote sensing Data processing Congresses. Télédétection Informatique Congrès. TECHNOLOGY & ENGINEERING Remote Sensing & Geographic Information Systems. bisacsh Remote sensing Data processing fast |
| title | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / |
| title_alt | Analysis of Multi-Temporal Remote Sensing Images Multitemp 2003 |
| title_auth | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / |
| title_exact_search | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / |
| title_full | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / editors, Paul C. Smits, Lorenzo Bruzzone. |
| title_fullStr | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / editors, Paul C. Smits, Lorenzo Bruzzone. |
| title_full_unstemmed | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / editors, Paul C. Smits, Lorenzo Bruzzone. |
| title_short | Proceedings of the Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images : |
| title_sort | proceedings of the second international workshop on the analysis of multi temporal remote sensing images multitemp 2003 joint research centre ispra italy 16 18 july 2003 |
| title_sub | Multitemp 2003, Joint Research Centre, Ispra, Italy, 16-18 July 2003 / |
| topic | Remote sensing Data processing Congresses. Télédétection Informatique Congrès. TECHNOLOGY & ENGINEERING Remote Sensing & Geographic Information Systems. bisacsh Remote sensing Data processing fast |
| topic_facet | Remote sensing Data processing Congresses. Télédétection Informatique Congrès. TECHNOLOGY & ENGINEERING Remote Sensing & Geographic Information Systems. Remote sensing Data processing Conference papers and proceedings |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=514745 |
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