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Discriminatively guided filtering (DGF) for hyperspectral image classification

Wang, Z; Hu, H; Zhang, L; Xue, J-H; (2018) Discriminatively guided filtering (DGF) for hyperspectral image classification. Neurocomputing , 275 pp. 1981-1987. 10.1016/j.neucom.2017.10.046. Green open access

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Abstract

In this paper, we propose a new filtering framework called discriminatively guided image filtering (DGF), for hyperspectral image (HSI) classification. DGF integrates a discriminative classifier and a generative classifier by the guided filtering (GF), considering the complementary strength of these two types of classification paradigms. To demonstrate the effectiveness of the proposed framework, the combination of support vector machine (SVM) and linear discriminative analysis (LDA), which serve as a discriminative classifier and a generative classifier respectively, is investigated in this paper. Specifically, the original HSI is projected into the low-dimensional space induced by LDA to serve as guidance images for filtering the intermediate classification results induced by SVM. Experiment results show the superior performance of the proposed DGF compared with that of the principal component analysis (PCA)-based GF.

Type: Article
Title: Discriminatively guided filtering (DGF) for hyperspectral image classification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neucom.2017.10.046
Publisher version: https://doi.org/10.1016/j.neucom.2017.10.046
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Hyperspectral image (HSI) classification, Guided image filtering (GF), Discriminative classifiers, Linear discriminant analysis (LDA), Principal component analysis (PCA), Support vector machine (SVM)
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10041444
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