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Active learning (AL) has obtained a great success in supervised remotely sensed hyperspectral image classification, since it is able to select highly informative training samples. As an intrinsically biased sampling approach, AL generally favors the selection of samples following discriminative distributions, which are located in low-density areas. However, hyperspectral data are often highly class-mixed,...
Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data...
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