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- W2901979315 abstract "Classification of a hyperspectral image (HSI) is a very active topic in remote sensing, which has practical applications in many fields, In this paper, a multitask sparse logistic regression method based on multi-feature and decision fusion approach (MTSLR-MPGF) is proposed for cross-scene hyperspectral image classification. Specifically, the Gabor Features with certain orientations and morphology feature are utilized and used for hyperspectral imagery classification. Next, we used feature fusion methods in order to produce an accurate thematic map based on the remote sensed hyperspectral image classification. The extensive experiments on two real hyperspectral data sets have demonstrated superior performance of the proposed MTSLR-MPGF approach over the state-of-the-art methods in terms of overall accuracy and average accuracy." @default.
- W2901979315 created "2018-11-29" @default.
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- W2901979315 date "2018-07-01" @default.
- W2901979315 modified "2023-10-17" @default.
- W2901979315 title "Multi-Feature-Based Decision Fusion Framework for Hyperspectral Imagery Classification" @default.
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- W2901979315 doi "https://doi.org/10.1109/igarss.2018.8518355" @default.
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