In order to obtain accurate classification results of hyperspectral images, both the spectral and spatial information should be fully exploited in the classification process. In this paper, we propose a novel method using independent component analysis (ICA) and edge-preserving filtering (EPF) via an ensemble strategy for the classification of hyperspectral data. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectrally independent components followed by an effective EPF method, to produce spatial features. Two strategies (i.e., parallel and concatenated) are presented to include the spatial features in the analysis. The spectral-spatial features are then classified with a random forest (RF) or rotation forest (RoF) classifier. Experimental results on two real hyperspectral datasets demonstrate the effectiveness of the proposed methods. A sensitivity analysis of the new classifiers is also performed.