Multiple kernel feature line embedding for hyperspectral image classification

In this study, a novel multple kernel FLE (MKFLE) based on general nearest feature line embedding (FLE) transformation is proposed and applied to classify hyperspectral image (HSI) in which the advantage of multple kernel learning is considered. The FLE has successfully shown its discriminative capability in many applications. However, since the conventional linear-based principle component analysis (PCA) pre-processing method in FLE cannot effectively extract the nonlinear information, the multiple kernel PCA (MKPCA) based on the proposed multple kernel method was proposed to alleviate this problem. The proposed MKFLE dimension reduction framework was performed through two stages. In the first multple kernel PCA (MKPCA) stage, the multple kernel learning method based on between-class distance and support vector machine (SVM) was used to find the kernel weights. Based on these weights, a new weighted kernel function was constructed in a linear combination of some valid kernels. In the second FLE stage, the FLE method, which can preserve the nonlinear manifold structure, was applied for supervised dimension reduction using the kernel obtained in the first stage. The effectiveness of the proposed MKFLE algorithm was measured by comparing with various previous state-of-the-art works on three benchmark data sets. According to the experimental results: the performance of the proposed MKFLE is better than the other methods, and got the accuracy of 83.58%, 91.61%, and 97.68% in Indian Pines, Pavia University, and Pavia City datasets, respectively.