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المؤلفون: Andrea Garzelli, L. Capobianco, Gustavo Camps-Valls
المصدر: IEEE transactions on geoscience and remote sensing 47 (2009): 3822–3833. doi:10.1109/TGRS.2009.2020910
info:cnr-pdr/source/autori:L. Capobianco; A. Garzelli; G. Camps-Valls/titolo:Target Detection with Semisupervised Kernel Orthogonal Subspace Projection/doi:10.1109%2FTGRS.2009.2020910/rivista:IEEE transactions on geoscience and remote sensing/anno:2009/pagina_da:3822/pagina_a:3833/intervallo_pagine:3822–3833/volume:47مصطلحات موضوعية: Graph, kernel method (KM), kernel orthogonal subspace projection (KOSP), manifold learning, regularization, semisupervised learning (SSL), target detection, business.industry, Matched filter, Nonlinear dimensionality reduction, Pattern recognition, Incomplete Cholesky factorization, Object detection, Kernel method, Kernel (image processing), Variable kernel density estimation, Robustness (computer science), General Earth and Planetary Sciences, Artificial intelligence, Electrical and Electronic Engineering, business, Mathematics
الوصف: 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 methods (KMs) makes the method nonlinear, helps to combat the high-dimensionality problem, and improves robustness to noise. This paper presents a semisupervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. Two further improvements are presented. First, a contextual selection of unlabeled samples is proposed. This strategy helps in better modeling the data manifold, and thus, improved sensitivity-specificity rates are obtained. Second, given the high computational burden involved, we present two alternative formulations based on the Nystroumlm method and the incomplete Cholesky factorization to achieve operational processing times. The good performance of the proposed method is illustrated in a toy data set and two relevant hyperspectral image target-detection applications: crop identification and thermal hot-spot detection. A clear improvement is observed with respect to the linear and the nonlinear kernel-based OSP, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in target-detection problems. The relevance of unlabeled samples and the computational cost are also analyzed in detail.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6dfa16b11d5b5f1f903f18f9f512238
http://hdl.handle.net/11365/24583