Laplacian Regularized Kernel Canonical Correlation Ensemble for Remote Sensing Image Classification

Abstract

Kernel canonical correlation analysis (KCCA) is an efficient dimensionality reduction tool in the application of remote sensing image classification. However, it suffers from the problem of parametric sensitivity since a single kernel is used. In this letter, a KCCA ensemble framework is put forward to improve the robustness of KCCA. Following the philosophy that two heads are better than one, multiple KCCA models are incorporated into the framework. And more importantly, their terms are weighted to adjust their contribution to the result according to their performance. In addition, over-fitting is overcome by introducing a Laplacian regularization term in our framework, hence, the name Laplacian regularized kernel canonical correlation ensemble. Experimental results on NWPU-RESISC45 data set show that our proposed method achieves better classification performances as compared to state-of-the-art methods in both shallow and deep features.