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Hyperspectral imaging (HSI) devices produce 3-D hyper-cubes of a spatial scene in hundreds of different spectral bands, generating large data sets which allow accurate data processing to be implemented. However, the large dimensionality of hypercubes leads to subsequent implementation of dimensionality reduction techniques such as principal component analysis (PCA), where the covariance matrix is...
Though hyperspectral imaging (HSI, or hypercube) has been applied in a wide range of applications, it suffers from massive volume of data for efficient data storage and transmission. Herein improved 3D discrete cosine transform (DCT) is proposed with good results yielded. Experiments on several datasets have validated the efficacy of our approach.
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