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.

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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.

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