Multiple features learning via rotation strategy
Images are usually represented by different groups of features, such as color, shape and texture attributes. In this paper, we propose a classification approach that integrates multiple features, such as spectral and spatial information. We refer this approach to multiple feature learning via rotation (MFL-R) strategy, which adopt a rotation-based ensemble method by using a data transformation approach. Five data transformation methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), linearity preserving projection (LPP) and multiple feature combination via manifold learning and patch alignment (MLPA) are used in the MFL-R framework. Experimental results over two hyperspectral remote sensing images demonstrate that MFL-R with MLPA gains better performances and is not sensitive to the tuning parameters.