NASA's upcoming HyspIRI mission — precision vegetation mapping with limited ground truth

摘要

Imaging spectrometers can acquire spectrally resolved images over a wide range of the electromagnetic spectrum. Availability of this rich spectral information makes it possible to design classification systems that can perform very 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 this work, we study the efficacy of HyspIRI observations in precision vegetation mapping applications under limited ground truth availability. In particular, sensitivity to mixed pixel conditions, temporal misalignments and the amount of training (ground-truth) data are studied for various state-of-the-art classification systems. This study will provide valuable insight on the relationship between the quality and quantity of available ground truth and performance of classification systems with these HyspIRI observations.