In this paper, an algorithm to randomly select feature subspaces for hyperspectral image classification using the principle of coalition game theory (CGT) is presented. The feature selection algorithms associated with nonlinear kernel-based support vector machines (SVM) are either NP-hard or greedy and hence, not very optimal. To deal with this problem, a metric based on the principles of CGT called Shapely value (SV) and a sampling approximation is used to determine the contributions of individual features toward the classification task. Feature subsets are randomly drawn from a probability distribution function (pdf) generated using normalized SVs of the individual features. These feature subsets are then used to build kernels corresponding to individual weak classifiers in the sparse kernel-based ensemble learning (SKEL) framework. By weighting the kernels optimally and sparsely, a small number of useful subsets of features are selected which improve the generalization performance of the ensemble classifier. The algorithm is applied on real hyperspectral datasets, and the results are presented in the paper.