MULTIPLE COMPOSITE KERNEL LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION

In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral and spatial information (e. g., extinction profiles). Then, the new spectral and spatial features are integrated into the composite kernels based on support vector machines classifier. Different rotation matrices will lead to obtaining various newly spectral and spatial characteristics, thereby they further increase the diversity and the classification performance. Experimental results on Indian Pines benchmark hyperspectral dataset demonstrate the excellent performance of the proposed method.