Chen Chen — Publications

J. Jiang, C. Chen, X. Song, and Z. Cai, “Hyperspectral Image Classification Using Set-to-Set Distance,” the 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, March 2016, accepted.
  • Abstract:
    Hyperspectral image (HSI) classification has attracted much attention and extensive research efforts over the past decade. Due to few labeled samples versus high dimensional features, it is a challenging problem in practice. Recently, combining the pixel spectral information and the spatial (neighborhood) information has been verified to be effective for HSI classification. In this paper, we introduce a novel method for HSI classification using set-to-set distance (SSD). Based on the assumption that neighbor pixels tend to belong to the same class with high probability, we model a test pixel and its neighbor pixels as a testing set (or a neighbor set) inspired by bilateral filtering. Meanwhile, the training pixels belong to the same class are modeled as a training set. Therefore, the classification is based on comparisons of sets distances. Experiments on a real HSI dataset show that our proposed method outperforms a number of existing state-of-the-art approaches.

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