The researchers developed a method called multiple kernel fuzzy discriminant analysis to improve the classification of hyperspectral images. By combining different types of kernel functions, they were able to better extract features and classify images compared to using just one type of kernel. Their experiment on AVIRIS images showed that this new method outperformed traditional fuzzy discriminant analysis with a single kernel.