Improving Linear Classification Using Semi-Supervised Invertible Manifold Alignment

Non-linear effects in hyperspectral data are the result of varying illumination conditions, angular dependencies of reflection, shadows and multiple scattering of incident light. Common classification algorithms like Spectral Angular Mapper (SAM) and Adaptive Coherence Estimator (ACE) struggle to produce good results under these conditions. In this paper, we evaluate our fast Semi-supervised Invertible Manifold Alignment, introduced in [1], on multiple commonly available hyperspectral remote sensing data sets. Additionally, we test it on our new benchmark data set for multitemporal analysis. We show that linear SAM classification on SIMA-transformed data is superior to linear classification on the original data in all cases. Also, SIMA-transformation with subsequent SAM classification produces comparable results to a multi-class Support Vector Machine (SVM), with the benefit of maintaining physical interpretability of the transformed data.