Spectral-Spatial Classification of Multispectral Images Using Kernel Feature Space Representation

Over the last few years, several new strategies have been proposed for spectral-spatial classification of remotely sensed image data, for cases when high spatial and spectral resolutions are available. In this letter, we focus on the possibility of performing advanced spectral-spatial classification of remote sensing images with limited spectral resolution (often called multispectral). A new strategy is proposed, where the spectral dimensionality of the multispectral data is first expanded by using nonlinear feature extraction with kernel methods such as kernel principal component analysis. Then, extended multiattribute profiles (EMAPs), built on the expanded set of spectral features, are used to include spatial information. This strategy allows us to first decompose different spectral clusters into different spectral features and further improve the spatial discrimination. The resulting EMAPs are used for classification using advanced classifiers such as support vector machines and random forests. We test our proposed methodology with different multispectral data sets obtaining state-of-the-art classification results.