A new approach to the application of conflict redistribution rule in Satellite Image Classification

Keywords: satellite image classification, combination rules, proportional conflict redistribution rule, conflicting evidence

Abstract

Nowadays solution of different scientific problems using satellite images, generally includes a classification procedure. Classification is one of the most important procedures used in remote sensing, because it involves a lot of mathematical operations and data preprocessing. The processing of information and combining of conflicting data is a very difficult problem in classification tasks. Nowadays many classification methods are applied in remote sensing. Classification of conflicting data has been a key problem, both from a theoretical and practical point of view. But a lot of known classification methods can not deal with highly conflicted data and uncertainty. The main purpose of this article is to apply proportional conflict redistribution rule (PRC5) for satellite image classification in conditions of uncertainty, when conflicting sources of evidence give incomplete and vague information. This rule can process conflicting data and combine conflicting bodies of evidence (spectral bands). Proportional conflict redistribution rule can redistribute the partial conflicting mass proportionally on non-empty sets involved in the conflict. It was noticed, that this rule can provide a construction of aggregated estimate under conflict. It calculates all partial conflicting masses separately. It was also shown, that proportional conflict redistribution rule is the most mathematically exact redistribution of conflicting mass to non-empty set. But this rule consists of difficult calculation procedures. The more hypotheses and more masses are involved in the fusion, the more difficult is to implement proportional conflict redistribution rule, therefore special computer software should be used. It was considered an example of practical use of the proposed conflict redistribution rule. It also was noticed, that this new approach to the application of conflict redistribution rule in satellite image classification can be applied for analysis of satellite images, solving practical and ecological tasks, assessment of agricultural lands, classification of forests, in searching for oil and gas.

References

Alpert М. І., Alpert S. І. (2019). A new approach to the application of Jaccard coefficient and Cosine similarity in Hyperspectral Image Classification. XVIII-th International Conference on Geoinformatics - Theoretical and Applied Aspects— Kiev, 13–16 May 2019. 1–5.

Barnett J. A. (1991). Calculating Dempster–Shafer plausibility. IEEE Trans. Pattern Anal. Machine Intell, 13. 599–602. Retrieved from https://doi.org/10.1109/34.87345

Beynon M. J. (2002). DS/AHP method; a mathematical analysis, including an understanding of uncertainty. European Journal of Operational Research, 140. 148–164.

Bongasser M. (2008). Hyperspectral Remote Sensing: Principles and Applications. Boca Raton, FL: CRC Press.

Chang C. I. (2013).Hyperspectral Data Processing: Algorithm Design and Analysis. Hoboken, NJ: John Willey & Sonsp.

Ferson S., Kreinovich V. (2002). Representation, Propagation, and Aggregation of Uncertainty. SAND Report.

Gong P. (1996). Integrated Analysis of Spatial Data from Multiple Sources: Using Evidential Reasoning and Artificial Neural Network Techniques for Geological Mapping. Photogrammetric Engineering and Remote Sensing.. Vol. 62, № 5. P. 513 – 523.

Lein J.K. Applying evidential reasoning methods to agricultural land cover classification. Int. Journal of Remote Sensing, 24 (21). 4161– 4180. Retrieved from https://doi.org/10.1080/0143116031000095916

McCoy R. M. (2005). Fields Methods in Remote Sensing. New York: Guilford Press. 150–160.

Mertikas P., Zervakis M. E. (2001). Exemplifying the Theory of Evidence in Remote Sensing Image Classification. Int. Journal of Remote Sensingm, 22 (6). 1081–1095. Retrieved from https://doi.org/10.1080/01431160118597.

Popov M., Alpert S., Podorvan V., Topolnytskyi M., Mieshkov S. (2015). Method of Hyperspectral Satellite Image Classification under Contaminated Training Samples Based on Dempster-Shafer’s Paradigm. Central European Researchers Journal, 1 (1). 86–97.

Renyi A. Probability theory (1970). Amsterdam, North – Holland Pub. Co.

Shafer G. (1990). Perspectives in the theory and practice of belief functions. Int. J. Approx. Reasoning, 4. 323–362.

Smarandache F. (2006). Proportional conflict redistribution rules for information fusion. American Research Press, 2. 61–103. Retrieved from https://doi.org/10.1016/0888-613x(93)90005-x.

Smets P. (1993). Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. Int. J. Approximate Reasoning, 9. 1–35. Retrieved from https://doi.org/10.1016/b978-0-444-88738-2.50010-5.

Smets P., Henrion M., Shachter R. D., Kanal L. N., Lemmer J. F. (1990). Constructing the pignistic probability function in a context of uncertainty. Uncertainty in Artificial Intelligence. North Holland, Amsterdam, 5. 29–40.

Yager R. (1987). On the Dempster-Shafer Framework and New Combination Rules. Information Sciences, 41. 93–137. Retrieved from https://doi.org/10.1016/0020-0255(87)90007-7.

Uzga-Rebrovs O. (2010). Nenoteiktiby parvaldisana. Resekne: RA Izdevnieciba, 3.

Zhang L., Yager R. R., Kacprzyk J., Fedrizzi M. (1994). Representation, independence, and combination of evidence in the Dempster-Shafer theory. Advances in the Dempster-Shafer Theory of Evidence. New York: John Wiley and Sons. Inc. 51–69. Retrieved from https://doi.org/10.1002/9781118445112.stat01573.

Section
Techniques for Earth observation data acquisition, processing and interpretation