A novel joint kernel principal component analysis (PCA) and relational perspective map (RPM) method called KPmapper is proposed for hyperspectral dimensionality reduction and spectral feature recognition. Kernel PCA is used to analyze hyperspectral data so that the major information corresponding to features can be better extracted. RPM is used to visualize hyperspectral data through two-dimensional (2D) maps, and it is an efficient approach to discover regularities and extract information by pa
