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• W1993457749 abstract "Errors associated with visual inspection and interpretations of radargrams often inhibitthe intensive surveying of widespread areas using ground-penetrating radar (GPR). Toautomate the interpretive process, this paper presents an application of a fuzzy-neural network(F-NN) classifier for unsupervised clustering and classification of soil profiles using GPRimagery. The classifier clusters and classifies soil profile strips along a traverse based oncommon pattern similarities that can relate to physical features of the soil (e.g., number ofhorizons; depth, texture and structure of the horizons; and relative arrangement of the horizons,etc). This paper illustrates this classification procedure by its application on GPR data, bothsimulated and actual real-world data. Results show that the procedure is able to classify theprofile into zones that corresponded with those obtained by visual inspection and interpretationof radargrams. Results indicate that an F-NN model can supply real-time soil profile clusteringand classification during field surveys." @default.
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• W1993457749 date "2002-01-01" @default.
• W1993457749 modified "2023-09-27" @default.
• W1993457749 title "Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery" @default.
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• W1993457749 doi "https://doi.org/10.13031/2013.11691" @default.
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