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21 November 1995 Unsupervised multisource remote sensing classification using Dempster-Shafer evidence theory
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Abstract
The aim of this paper is to show that Dempster-Shafer evidence theory may be successfully applied to unsupervised classification in multisource remote sensing. The main advantage of unsupervised classification is that no a priori information is needed. Dempster-Shafer formulation allows the user to consider union of classes, and to represent both imprecision and incertitude. So, it provides better representation of sensor information and more reliable classification results. An unsupervised multisource classification algorithm is applied to Mac- Europe'91 multi-sensor airborne campaign data. Classification results using different combinations of sensors (TMS and AirSAR) or wavelengths L and C bands) are compared. Performance of data fusion has been evaluated in terms of identification of culture types. Particularly, we show that, even if most performing results were achieved using the three data sets, good identification rates could be obtained using less expensive combinations of data.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Mascle, Isabelle Bloch, and D. Vidal-Madjar "Unsupervised multisource remote sensing classification using Dempster-Shafer evidence theory", Proc. SPIE 2584, Synthetic Aperture Radar and Passive Microwave Sensing, (21 November 1995); https://doi.org/10.1117/12.227129
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