A Subspace-Based Multinomial Logistic Regression for Hyperspectral Image Classification
In this letter, we propose a multinomial-logistic-regression method for pixelwise hyperspectral classification. The feature vectors are formed by the energy of the spectral vectors projected on class-indexed subspaces. In this way, we model not only the linear mixing process that is often present in the hyperspectral measurement process but also the nonlinearities that are separable in the feature space defined by the aforementioned feature vectors. Our experimental results have been conducted using both simulated and real hyperspectral data sets, which are collected using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Reflective Optics System Imaging Spectrographic (ROSIS) system. These results indicate that the proposed method provides competitive results in comparison with other state-of-the-art approaches.