14 December 1999 Uncertainty management in neural classifiers of remotely sensed data
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Abstract
This paper presents a novel neural model based on back- propagation for fuzzy Dempster-Shafer (FDS) classifiers. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster-Shafer theory. In this context the learning task may be formulated as the search the most adequate 'ingredients' of the fuzzy and Dempster-Shafer frameworks such as the fuzzy aggregation operators for fusing data from different sources and focal elements and basic probability assignments for describing the contributions of evidence in the inference scheme. The new neural model allows to establish a complete correspondence between connectionist elements and fuzzy and Dempster-Shafer ingredients ensuring both a high level of interpretability and transparency and high performances in classification. A network-to-rule translation procedure is allowed for extracting Fuzzy Dempster-Shafer classification rules from the structure of the trained network. To evaluate the performances in real domains where the conditions of lack of specificity in data are prevalent, the proposed model has been applied to a multisource remote sensing classification problem. The numerical results are shown here and compared with those obtained by symbolic FDS and pure neuro-fuzzy classification procedure.
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Elisabetta Binaghi, Elisabetta Binaghi, Paolo Madella, Paolo Madella, Ignazio Gallo, Ignazio Gallo, Monica Pepe, Monica Pepe, Anna Rampini, Anna Rampini, } "Uncertainty management in neural classifiers of remotely sensed data", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373258; https://doi.org/10.1117/12.373258
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