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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 conditions. However, the computational complexity of the l ₁ -norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery (HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative representation but not the l ₁ -norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D Gabor feature …