feature selection; geophysical image processing; hyperspectral imaging; image classification; parameter estimation; support vector machines; AD 2012; APS; FRBF kernel; Highes phenomenon; Indian Pine Site dataset; SVM; automatic kernel parameter selection; black-box model; full bandwidth RBF; high-dimensional data classification problem; hyperspectral image classification; kernel parameters; kernel separability measure; nonlinear feature selection method; nonlinear support vector machine; z-scores; Accuracy; Bandwidth; Hyperspectral imaging; Image classification; Kernel; Support vector machines; Training; Feature selection; SVM;
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