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Spectral Mixture Analysis of Hyperspectral Scenes Using Intelligently Selected Training Samples

In this letter, we address the use of artificial neural networks for spectral mixture analysis of hyperspectral scenes. We specifically focus on the issue of how to effectively train neural network architectures in the context of spectral mixture analysis applications. To address this issue, a multilayer perceptron neural architecture is combined with techniques for intelligent selection and labeling of training samples directly obtained from the input data, thus maximizing the information that can be obtained from those samples while reducing the need for a priori information about the scene. The proposed approach is compared to unconstrained and fully constrained linear mixture models using hyperspectral data sets acquired (in the laboratory) from artificial forest scenes, using the compact airborne spectrographic imaging system. The Spreading of Photons for Radiation INTerception (SPRINT) canopy model, which assumes detailed knowledge about object geometry, was employed to evaluate the results obtained by the different methods. Our results show that the proposed approach, when trained with both pure and mixed training samples (generated automatically without prior information) can provide similar results to those provided by SPRINT, using very few labeled training samples. An application to real airborne data using a set of hyperspectral images collected at different altitudes by the digital airborne imaging spectrometer 7915 and the reflective optics system imaging spectrometer, operating simultaneously at multiple spatial resolutions, is also presented and discussed.