Data combination method in Remote Sensing tasks in case of conflicting information sources

  • Sofiia Alpert Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
Keywords: evidence theory, Jaccard coefficient, basic probability assignment, combination rules, conflicting evidence


Nowadays technologies of UAV-based Remote Sensing are used in different areas, such as: ecological monitoring, agriculture tasks, exploring for minerals, oil and gas, forest monitoring and warfare. Drones provide information more rapidly than piloted aerial vehicles and give images of a very high resolution, sufficiently low cost and high precision.
Let’s note, that processing of conflicting information is the most important task in remote sensing. Dempster’s rule of data combination is widely used in solution of different remote sensing tasks, because it can processes incomplete and vague information. However, Dempster’s rule has some disadvantage, it can not deal with highly conflicted data. This rule of data combination yields wrong results, when bodies of evidence highly conflict with each other. That’s why it was proposed a data combination method in UAV-based Remote Sensing. This method has several important advantages: simple calculation and high accuracy.
In this paper data combination method based on application of Jaccard coefficient and Dempster’s rule of combination is proposed. The described method can deal with conflicting sources of information. This data combination method based on application of evidence theory and Jaccard coefficient takes into consideration the associative relationship of the evidences and can efficiently handle highly conflicting sources of data (spectral bands).
The frequency approach to determine basic probability assignment and formula to determine Jaccard coefficient are described in this paper too. Jaccard coefficient is defined as the size of the intersection divided by the size of the union of the sample sets. Jaccard coefficient measures similarity between finite sets. Some numerical examples of calculation of Jaccard coefficient and basic probability assignments are considered in this work too.
This data combination method based on application of Jaccard coefficient and Dempster’s rule of combination can be applied in exploring for minerals, different agricultural, practical and ecological tasks.


Alpert, S. (2020). A new approach to applying the discount rule in hyperspectral satellite image classification. Management of Development of Complex Systems, 43. 76–82. doi.org10.32347/2412-9933.2020.43.76-82.

Alpert, М. І., Alpert, S .І. (2020). New methods to determine basic probability assignment and data fusion in Hyperspectral Image Classification. Proceedings of the XIX-th International Conference on Geoinformatics. Theoretical and Applied Aspects. 1–5.

Alpert. М. І., Alpert, S. І. (2021). A new approach to accuracy assessment of land-cover classification in UAV-based Remote Sensing. Proceedings of the XXth International Conference on Geoinformatics Theoretical and Applied Aspects. 1–5.

Jousselme, Anne-Laure. Grenier, Dominic, Bosse,. Eloi. (2001). A new distance between two bodies of evidence. Information Fusion, 2. 91–101.

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, 62 (5). 513–523.

Kosub S. (2019). A note on the triangle inequality for the Jaccard distance. Pattern Recognition Letters. 120. 36–38.

Lein, J. K. (2003). Applying evidential reasoning methods to agricultural land cover classification. Int. Journal of Remote Sensing, 24 (21). 4161–4180.

McKnight, V. (2015). Drone technology and the Fourth Amendment: aerial surveillance precedent and Kyllo do not account for current technology and privacy concerns. California Western Law Review, 51. 263.

Mertikasm P., Zervakism M. E. (2001). Exemplifying the Theory of Evidence in Remote Sensing Image Classification, Int. Journal of Remote Sensing. 22 (6). 1081–1095.

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.

Popov, M .A., Alpert, S .І., Podorvan, V. N. (2017). Satellite image classification method using the Dempster-Shafer approach. Izvestiya, atmospheric and oceanic. Physics, 53 (9). 1112–1122. doi: 10.1134/s0001433817090250

Earth observation data applications: Challenges and tasks