Spectral variability remains a challenging problem for target detection in hyperspectral (HS) imagery. In previous work, we developed a target detection scheme using the kernel-based support vector data description (SVDD). We constructed a first-order Markov-based Gaussian model to generate samples to describe the spectral variability of the target class. However, the Gaussian-generated samples also require selection of the variance parameter σ2 that dictates the level of variability in the generated target class signatures. In this work, we have investigated the use of decision-level fusion techniques for alleviating the problem of choosing a proper value of σ2. We have trained a collection of SVDDs with unique variance parameters σ2 for each of the target training sets and have investigated their combination using the traditional AND, OR, and majority vote (MV) decision-level rules. We have inserted target signatures into an urban HS scene with differing levels of spectral variability to explore the performance of the proposed scheme in these scenarios. Experiments show that the MV fusion rule is the best choice, providing relatively low false positive rates (FPR) while yielding high true positive rates (TPR). Detection results show that the proposed SVDD-based decision-level scheme using the MV fusion rule is highly accurate and yields higher true positive rates (TPR) and lower false positive rates (FPR) than the adaptive matched filter (AMF).