Hyperspectral image xlassification using a general NFLE transformation with kernelization and fuzzification

Abstract

Nearest feature line (NFL) embedding (NFLE) is an eigenspace transformation algorithm based on the NFL strategy. Based on this strategy, the NFLE algorithm generates a low dimensional space in which the local structures of samples in the original high dimensional space are preserved. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. To address this, a general NFLE transformation, called fuzzy/kernel NFLE, is proposed for feature extraction in which kernelization and fuzzification are simultaneously considered. In the proposed scheme, samples are projected into a kernel space and assigned larger weights based on that of their neighbors according to their neighbors. In that way, not only is the non-linear manifold structure preserved, but also are the discriminative powers of classifiers increased. The proposed method is compared with various state-of-the-art methods to evaluate the performance by several benchmark data sets. From the experimental results, the proposed FKNFLE outperformed the other, more conventional, methods.