Data exploitation of HyspIRI observations for precision vegetation mapping

An imaging spectrometer's dense recording of radiance values (and consequently derived reflectance values) over a wide region of the electromagnetic spectrum provides the potential to design classification systems that can perform highly accurate ground cover classification and target recognition. The Hyperspectral Infrared Imager (HyspIRI) - a National Research Council (NRC) decadal survey mission is much anticipated by researchers to aid in answering a wide variety of global ecological and anthropological questions. In order to tackle these research topics and effectively exploit the seasonal global imaging spectroscopy provided by HyspIRI, there will be a great need for reliable hyperspectral-based products for use by domain experts. In this work, we create simulated/proxy HyspIRI data from a database of hyperspectral signatures of various vegetation species. We then study the performance of current state-of-the-art pattern classification paradigms for classifying this proxy data. The outcome of this study will provide valuable insight into the potential efficacy of employing HyspIRI data for vegetation mapping and similar remotely sensed pattern classification tasks.