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DER: Dynamic Evidential Reasoning applied to hyperspectral images classification
Keywords:
Hyperspectral analysis, Evidential reasoning, Crops classificationAbstract
This paper describes a new classification method (DER) based on evidential reasoning to which a series of modifications are added [1]. DER allows including new evidence for the classification process and defines a different decision rule. The evidential reasoning algorithm provides a means to combine evidence from different data sources. It is a supervised classification technique that uses a training samples set. This novel method (DER) offers a learning stage to introduce new evidence in case the classifier requires so. Moreover, it uses the plausibility measure in order to define the decision rule as a way to incorporate data-associated uncertainty. The proposed method is applied in order to classify crops in hyperspectral images of the area of Nebraska (USA). Some results obtained are presented in order to assess DER precision.
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References
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[2] Jensen. “Introductory Digital Image Processing. A remote sensing perspective”, 2da Edition, Prentice Hall. 1996
[3] A. F. H. Goetz , and V. Srivastava, "Mineralogical mapping in the Cuprite Mining District, Nevada", in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena, CA, pp. 22-29. 1985
[4] T. M. Lillesand, R. W. Kiefer. "Remote Sensing and Image Interpretation", 3rd Edition, John Wiley. 1994.
[5] D. Peddle, and S. Franklin. “Multisource evidential classification of surface cover and frozen ground”. International Journal R. S., vol. 13- No. 17. 1992
[6] D. Peddle, and S. Franklin. “Classification of Permafrost Active Layer Depth from Remotely Sensed and Topographic Evidence”, Remote Sensing Environment, vol. 44, No.1, pp. 67-80. 1993.
[7] T. Lee, J. Richards, and P. Swain. “Probabilistic and Evidential Approaches for Multi-source Data Analysis”, IEEE Transactions on Geoscience and Remote Sensing, vol. 25, No. 3, pp. 283-292. 1987
[8] G. Wilkinson, and J. Megier. “Evidential Reasoning in a Pixel Classification Hierarchy – A Potential Method for Integrating Image Classifiers and Expert System Rules Based on Geographic Context”, International Journal of Remote Sensing, vol. 11, No.10, pp. 1963-1968. 1990
[9] H. Kim, and P. Swain. “A Method for Classification of Multisource Data Using Interval- Valued Probabilities and its Applications to Hiris Data”, in Proceedings of a Workshop on Multisource Data Integration in Remote Sensing, NASA Conference Publication 3099, pp. 75-81. 1990.
[10] A. Srinivasan, and J. Richards. “Knowledge-based Techniques for Multi-source Classification”, International Journal of Remote Sensing, vol.11, No.3, pp.505-525. 1990.
[11] D. Peddle. “Knowledge Formulation for Supervised Evidential Classification”. Photogrammetric Engineering & Remote Sensing, vol.61, No.4., pp. 409-417. 1995.
[12] D. Peddle. “An Empirical comparison of evidential reasoning, linear discriminant analysis, and maximum likelihood algorithms for alpine land cover classification”. Canadian Journal of Remote Sensing, vol.19, No.1. 1993.
[13] Anger C.D., Mah, S., Babey, S.K. "Technological enhancements to the compact airborne spectrographic imager (casi)." In Proceedings of the First International Airborne Remote Sensing Conference and Exhibition. Strasbourg, France. Vol. II, pp. 205-213. 1994.
[14] Sanz C., “DER (Dynamic Evidential Reasoning), applied to the classification of hyperspectral images”. International Geoscience and Remote Sensing Symposium (IGARSS 2001 – IEEE), July,
2001. ISBN: 0-7803-7033-3
[15] Babey, S.K., Anger, C.D. "Compact airborne spectrographic imager (casi): A progress review." In Proceedings of the SPIE Conference. Orlando, Florida. SPIE Vol. 1937, pp. 152-163. 1993.
[16] R. G. Congalton, K. Green. "Assessing the Accuracy of Remotely Sensed Data: Principles and Practices". Lewis Publishers. 1997.
[17] Jensen. “Introductory Digital Image Processing. A remote sensing perspective”. 2da edition. Prentice Hall. 1996.
[18] "Remote Sensing Digital Image Analysis: An Introduction". J. A. Richards, X. Jia. SpringerVerlag New York, Incorporated. 1999.
[2] Jensen. “Introductory Digital Image Processing. A remote sensing perspective”, 2da Edition, Prentice Hall. 1996
[3] A. F. H. Goetz , and V. Srivastava, "Mineralogical mapping in the Cuprite Mining District, Nevada", in Proceedings of the Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publication 85-41, Jet Propulsion Laboratory, Pasadena, CA, pp. 22-29. 1985
[4] T. M. Lillesand, R. W. Kiefer. "Remote Sensing and Image Interpretation", 3rd Edition, John Wiley. 1994.
[5] D. Peddle, and S. Franklin. “Multisource evidential classification of surface cover and frozen ground”. International Journal R. S., vol. 13- No. 17. 1992
[6] D. Peddle, and S. Franklin. “Classification of Permafrost Active Layer Depth from Remotely Sensed and Topographic Evidence”, Remote Sensing Environment, vol. 44, No.1, pp. 67-80. 1993.
[7] T. Lee, J. Richards, and P. Swain. “Probabilistic and Evidential Approaches for Multi-source Data Analysis”, IEEE Transactions on Geoscience and Remote Sensing, vol. 25, No. 3, pp. 283-292. 1987
[8] G. Wilkinson, and J. Megier. “Evidential Reasoning in a Pixel Classification Hierarchy – A Potential Method for Integrating Image Classifiers and Expert System Rules Based on Geographic Context”, International Journal of Remote Sensing, vol. 11, No.10, pp. 1963-1968. 1990
[9] H. Kim, and P. Swain. “A Method for Classification of Multisource Data Using Interval- Valued Probabilities and its Applications to Hiris Data”, in Proceedings of a Workshop on Multisource Data Integration in Remote Sensing, NASA Conference Publication 3099, pp. 75-81. 1990.
[10] A. Srinivasan, and J. Richards. “Knowledge-based Techniques for Multi-source Classification”, International Journal of Remote Sensing, vol.11, No.3, pp.505-525. 1990.
[11] D. Peddle. “Knowledge Formulation for Supervised Evidential Classification”. Photogrammetric Engineering & Remote Sensing, vol.61, No.4., pp. 409-417. 1995.
[12] D. Peddle. “An Empirical comparison of evidential reasoning, linear discriminant analysis, and maximum likelihood algorithms for alpine land cover classification”. Canadian Journal of Remote Sensing, vol.19, No.1. 1993.
[13] Anger C.D., Mah, S., Babey, S.K. "Technological enhancements to the compact airborne spectrographic imager (casi)." In Proceedings of the First International Airborne Remote Sensing Conference and Exhibition. Strasbourg, France. Vol. II, pp. 205-213. 1994.
[14] Sanz C., “DER (Dynamic Evidential Reasoning), applied to the classification of hyperspectral images”. International Geoscience and Remote Sensing Symposium (IGARSS 2001 – IEEE), July,
2001. ISBN: 0-7803-7033-3
[15] Babey, S.K., Anger, C.D. "Compact airborne spectrographic imager (casi): A progress review." In Proceedings of the SPIE Conference. Orlando, Florida. SPIE Vol. 1937, pp. 152-163. 1993.
[16] R. G. Congalton, K. Green. "Assessing the Accuracy of Remotely Sensed Data: Principles and Practices". Lewis Publishers. 1997.
[17] Jensen. “Introductory Digital Image Processing. A remote sensing perspective”. 2da edition. Prentice Hall. 1996.
[18] "Remote Sensing Digital Image Analysis: An Introduction". J. A. Richards, X. Jia. SpringerVerlag New York, Incorporated. 1999.
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Published
2002-05-01
How to Cite
Sanz, C. V., & Jordán, R. (2002). DER: Dynamic Evidential Reasoning applied to hyperspectral images classification. Journal of Computer Science and Technology, 1(06), 8 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/967
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