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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Capobianco, L. Garzelli, A. Camps-Valls, G. |
| Copyright Year | 2009 |
| 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. |
| Starting Page | 566 |
| Ending Page | 571 |
| File Size | 466754 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424447350 |
| DOI | 10.1109/ISDA.2009.121 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hyperspectral sensors Remote sensing Matched filters target detection Object detection Detectors Machine learning Signal processing Libraries Kernel Intelligent systems hyperspectral images Hyperspectral imaging |
| Content Type | Text |
| Resource Type | Article |
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