The Orthogonal Subspace Projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel helps to combat the high dimensionality problem and makes the method robust to noise. This paper presents a semi-supervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a toy dataset and an hyperspectral image target detection problem.

Capobianco, L., Garzelli, A., & CAMPS VALL, G. (2008). Semi-supervised kernel orthogonal subspace projection. In Proc. IEEE IGARSS 2008 (pp.216-219). IEEE [10.1109/IGARSS.2008.4779696].

Semi-supervised kernel orthogonal subspace projection

GARZELLI, ANDREA;
2008

Abstract

The Orthogonal Subspace Projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel helps to combat the high dimensionality problem and makes the method robust to noise. This paper presents a semi-supervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. The good performance of the proposed method is illustrated in a toy dataset and an hyperspectral image target detection problem.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/5476
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