Learning Based Atmospheric Compensation: Results on Prisma Data

In this work we extend the recently proposed Learning-Based approach to Atmospheric Compensation (LBAC) with respect to two different aspects: 1) the platform for data acquisition and 2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensor operating in the Visible Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. Results obtained on PRISMA hyperspectral images are presented and discussed.