A semi-supervised graph-based approach to target detection is presented. The proposed method improves the Kernel Orthogonal Subspace Projection (KOSP) by deforming the kernel through the approximation of the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a hyperspectral image target detection application for thermal hot spot detection. An improvement is observed with respect to the linear and the non-linear kernel-based OSP, demonstrating good generalization capabilities when low number of labeled samples are available, which is usually the case in target detection problems.
Capobianco, L., Garzelli, A., Camps-Valls, G. (2009). Semi-supervised kernel target detection in hyperspectral images. In Proc. ISDA'09 (pp.566-571). New York : Elsevier [10.1109/ISDA.2009.121].
Semi-supervised kernel target detection in hyperspectral images
Garzelli, Andrea;
2009-01-01
Abstract
A semi-supervised graph-based approach to target detection is presented. The proposed method improves the Kernel Orthogonal Subspace Projection (KOSP) by deforming the kernel through the approximation of the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a hyperspectral image target detection application for thermal hot spot detection. An improvement is observed with respect to the linear and the non-linear kernel-based OSP, demonstrating good generalization capabilities when low number of labeled samples are available, which is usually the case in target detection problems.
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https://hdl.handle.net/11365/5490
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