This letter presents a novel superpixel-based approach to hyperspectral image analysis which exploits spatial context within spectrally similar contiguous pixels for robust hyperspectral classification. The proposed approach entails two key steps-first, as a preprocessing step, we compute groupings (superpixels) through graph-based segmentation, following which an object-level classification is undertaken using a decision fusion approach that merges per-pixel outcomes from an ensemble of “per-pixel” Bayesian classifiers. The proposed method provides a robust way to exploit spatial contextual information. Every pixel in a superpixel is classified using statistical Bayesian classification independently, and the decisions are merged to obtain a unique class label for each superpixel. Experimental results with hyperspectral imagery indicate that the proposed method consistently provides a robust classification framework, even when using very limited training data.