Probabilistic rough set-based band selection method for hyperspectral data classification Online publication date: Sat, 22-Feb-2020
by Min Li; Shaobo Deng; Lei Wang; Jun Ye
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 1, 2020
Abstract: This paper proposes an innovative band selection algorithm called probabilistic rough set-based band selection (PRSBS) algorithm. The proposed PRSBS is a supervised band selection algorithm with efficiency for it only needs to calculate the first-order significance measure. The main novelty of the proposed PRSBS algorithm lies in the criterion function which measures the effectiveness of considered band. The PRSBS algorithm uses a probabilistic distribution dependency as the relevance measure between the bands and class labels, which can effectively measure the uncertainty in both the positive and the boundary samples in a dataset. We compared the proposed PRSBS with the most relevant band selection algorithm RSBS on three different hyperspectral datasets, the experimental results show that the PRSBS has better results than the RSBS. Moreover, the PRSBS algorithm runs significantly faster than the RSBS algorithm, which makes it a proper choice for band selection in hyperspectral image dataset.
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