Spatial information has been demonstrated to be useful for hyperspectral images (HSIs) classification. The challenge is that spatial properties are often present at various spatial scales instead of a single fixed scale. A multiscale conservative smoothing algorithm is proposed in this paper to reduce noise and extract spatial structure information from coarse to fine levels. Over-smoothing is prevented automatically by imposing a weighting scheme on the neighboring pixels used for smoothing, where dissimilar neighbors' contributions are suppressed. Motived by multitask learning, an adaptive sparse representation is introduced to integrate different characteristics from the series of enhanced HSIs. The sparse coefficients of a given unknown pixel can be obtained from this representation and then used for classification. Experiments conducted on three benchmark data sets demonstrate that the proposed methodology leads to superior classification performance when compared to several well-known classifiers.