Local binary pattern (LBP) is a simple yet efficient image feature description algorithm that has been widely used in applications related to image classification. To more effectively make use of the local features of a hyperspectral image (HSI), a tensor-based spatial–spectral local binary pattern (TSSLBP) that can fully utilize the local spatial-spectral feature of an HSI is proposed. We specifically make efforts in three aspects. First, we construct a third-order tensor model for HSI to combine the local spatial and spectral features. Second, a dimension reduction method based on tensor decomposition is proposed to reduce redundant information of the HSI tensor model. Third, a joint spatial-spectral LBP coding algorithm is implemented based on the HSI tensor model to calculate the local spatial–spectral feature vector of HSI. To verify the effectiveness of the proposed TSSLBP, we apply it to the task of HSI classification and compare it with six existing algorithms. Experimental results on the three popular HSI datasets show that TSSLBP can fully utilize rich information in the local spatial–spectral domain and can significantly improve the classification accuracy of HSI, especially in the case of scarce training samples.
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