29 October 2007 Introducing training and parameter tuning for KOSP classification of hyperspectral images
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Abstract
Kernel-based Orthogonal Subspace Projection (KOSP) provides good results in the field of classification of hyperspectral images. However, an open-problem is the evaluation from the ground-truth samples of the prototypes that best represent the classes. In the original formulation of KOSP, this preliminary (training) stage is very simple since for each class the prototype is computed as the centroid of the ground-truth samples. In order to improve KOSP performances, in this paper we introduce a minimization problem to evaluate the best prototypes from a given ground truth of a specific classification problem. K-fold cross-validation is used to avoid overfitting. The performance of the proposed methodology is tested by classifying the widely used 'Indian Pine' hyperspectral dataset collected by the AVIRIS spectrometer.
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L. Capobianco, L. Capobianco, L. Carli, L. Carli, A. Garzelli, A. Garzelli, F. Nencini, F. Nencini, } "Introducing training and parameter tuning for KOSP classification of hyperspectral images", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480B (29 October 2007); doi: 10.1117/12.738494; https://doi.org/10.1117/12.738494
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