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The rich information available in hyperspectral imagery not only poses significant opportunities but also makes big challenges for material classification. Discriminative features seem to be crucial for the system to achieve accurate and robust performance. In this paper, we propose a 3-D Gabor-wavelet-based approach for pixel-based hyperspectral imagery classification. A set of complex Gabor wavelets with different frequencies and orientations is first designed to extract signal variances in space, spectrum, and joint spatial/spectral domains. The magnitude of the response at each sampled location (x, y) for spectral band b contains rich information about the signal variances in the local region. Each pixel can be well represented by the rich information extracted by Gabor wavelets. A feature selection and fusion process has also been developed to reduce the redundancy among Gabor features and make the fused feature more discriminative. The proposed approach was fully tested on two real-world hyperspectral data sets, i.e., the widely used Indian Pine site and Kennedy Space Center. The results show that our method achieves as high as 96.04% and 95.36% accuracies, respectively, even when only few samples, i.e., 5% of the total samples per class, are labeled.