The developments in sensor technology have made the high-resolution hyperspectral remote sensing data available to the remote sensing analyst for ground-cover classification and target recognition tasks. The inherent high dimensionality of such data sets and the limited ground-truth data availability in many real-life operating scenarios necessitate such hyperspectral classification systems to employ the dimensionality reduction algorithms. Previously, it has been shown that the addition of the spectral derivatives into the feature space improves the performance of the hyperspectral image analysis systems. Although the spectral derivative features are expected to provide additional information for the classification task at hand, the conventional classification techniques are typically not suitable for such fusion since simply combining these features would result in very high dimensional feature spaces, exacerbating the over-dimensionality problem. In this paper, we propose an effective approach for the decision-level fusion of the spectral reflectance information with the spectral derivative information for robust land cover classification. This paper differs from previous work because we propose effective classification strategies to alleviate the increased over-dimensionality problem introduced by the addition of the spectral derivatives for hyperspectral classification. The studies reported in this paper are conducted within the context of both single and multiple classifier systems that are designed to handle the high-dimensional feature spaces. The experimental results are reported with handheld, airborne, and spaceborne hyperspectral data. The efficacy of the proposed approaches (using spectral derivatives and single or multiple classifiers) as quantified by the overall classification accuracy (expressed in percentage) is significantly greater than that of these systems when exploiting only the reflectance information.