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Document is unavailable: This DOI was registered to an article that was not presented by the author(s) at this conference. As per section 8.2.1.B.13 of IEEE's "Publication Services and Products Board Operations Manual," IEEE has chosen to exclude this article from distribution. We regret any inconvenience.
Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, which has successfully been applied to several pattern recognition problems. According to compressive sensing theory, the $l_{1}$-norm minimization could yield the same sparse solution as the $l_{0}$ norm under certain...
In this paper, an effective l2-norm collaborative representation algorithm based on 3D discrete wavelet transform (3D-DWT) features, called CR_DWT, is proposed for hyperspec-tral image classification. By using the discriminative 3D-DWT features extracted from the original spectral space, a non-parametric and efficient l2-norm CR method is developed to calculate the representation coefficients. Due...
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