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- W2766814148 abstract "In this paper, we proposed a spectral spatial filtering (SSF) method by employing the spectral angle distance between central pixel and its neighboring pixels in the window. And its performance was verified using different classifiers of hyperspectral images. Classification results show that SSF method is able to smooth the image effectively while keep detailed information at the same time. SSF can significantly improve the classification accuracy for these classifiers, in which the SSF-Maximum Likelihood achieves the best result with the highest overall accuracy and kappa coefficient. It shows great performance in distinguishing some classes, which are difficult for other classifiers. We also test the adaptability of SSF to classification of small sample by using Support Vector Machine classifier, and reach to some encouraging results as expected." @default.
- W2766814148 created "2017-11-10" @default.
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- W2766814148 date "2014-06-01" @default.
- W2766814148 modified "2023-09-27" @default.
- W2766814148 title "A novel spectral spatial filtering approach for hyperspectral image classification" @default.
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- W2766814148 doi "https://doi.org/10.1109/whispers.2014.8077514" @default.
- W2766814148 hasPublicationYear "2014" @default.
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