Efficient Kernel Orthonormalized PLS for Remote Sensing Applications
Jerónimo Arenas-Garcia and Gustavo Camps-Valls
IEEE Transactions on Geoscience and Remote Sensing
This paper studies the performance and applicability of a novel Kernel Partial Least Squares (KPLS) algorithm for non-linear feature extraction in the context of remote sensing applications. The so-called Kernel Orthonomalized PLS algorithm with reduced complexity (rKOPLS) has two core parts: (i) a kernel version of OPLS (called KOPLS), and (ii) a sparse approximation for large scale data sets, which ultimately leads to the rKOPLS algorithm. The method is theoretically analyzed in terms of computational and memory requirements, and tested in common remote sensing applications: multi- and hyperspectral image classification and biophysical parameter estimation problems. The proposed method largely outperforms the traditional (linear) PLS algorithm, and demonstrates good capabilities in terms of expressive power of the extracted non-linear features, accuracy and scalability as compared to the standard KPLS.