A Novel Multiple Kernel Learning Framework for Remote Sensing Scene Classification

In the paper we propose a novel multiple kernel learning framework for representation-based classification (MKL-RC) of remote sensing image scenes. Unlike the existing methods that often greedily learn an optimal combined kernel from predefined base kernels by optimization method, resulting in high computation time but relatively better performance. The proposed approach is different from traditional kernel methods and characterized by multiple feature and multiple kernel learning in a representation-based classification manner. Experimental results on two real remote sensing scene datasets demonstrate that the proposed methods can achieve superior performance than the state-of-the-art classification methods.