Multiple kernel (MK) learning (MKL) methods have a significant impact on improving the classification performance. Besides that, composite kernel (CK) methods have high capability on the analysis of hyperspectral images due to making use of the contextual information. In this work, it is aimed to aggregate both CKs and MKs autonomously without the need of kernel coefficient adjustment manually. Convex combination of predefined kernel functions is implemented by using multiple kernel extreme learning machine. Thus, complex optimization processes of standard MKL are disposed of and the facility of multi-class classification is profited. Different types of kernel functions are placed into MKs in order to realize hybrid kernel scenario. The proposed methodology is performed over Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground-truth information. Multiple composite kernels are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters, and then the obtained results are presented comparatively along with the state-of-the-art standard machine learning, MKL, and CK methods.