Adaptive Multiple Kernel Self-organizing Maps for Hyperspectral Image Classification

Classification of hyperspectral images is a hot topic in remote sensing field because of its immense dimensionality. Several machine learning approaches had been effectively proposed for hyperspectral image processing. Multiple kernel learning (MKL) approaches are the most used techniques that have been promoted to improve the adaptability of kernel based learning machine. In this paper, an adaptive MKL approach is promoted for the classification of hyperspectral imagery problem. The core idea in the introduced algorithm is to optimize the convex combinations of the given base kernels during the training process of Self-Organizing Maps. Diverse types of kernel functions are used. The performance of the classifier based on the choice of the kernel function and its variables, Benchmark hyperspectral datasets are used. The experimental results demonstrate that the introduced MKLSOM learning algorithm gives a comparative solution to the state-of-the-art algorithms.