Spatial response resampling (SR(2)): Accounting for the spatial point spread function in hyperspectral image resampling

With the increased availability of hyperspectral imaging (HSI) data at various scales (0.03–30 m), the role of simulation is becoming increasingly important in data analysis and applications. There are few commercially available tools to spatially degrade imagery based on the spatial response of a coarser resolution sensor. Instead, HSI data are typically spatially degraded using nearest neighbor, pixel aggregate or cubic convolution approaches. Without accounting for the spatial response of the simulated sensor, these approaches yield unrealistically sharp images. This article describes the spatial response resampling (SR(2)) workflow, a novel approach to degrade georeferenced raster HSI data based on the spatial response of a coarser resolution sensor. The workflow is open source and widely available for personal, academic or commercial use with no restrictions. The importance of the SR(2) • The SR(2) workflow derives the point spread function of a specified HSI sensor based on nominal data acquisition parameters (e.g., integration time, altitude, speed), convolving it with a finer resolution HSI dataset for data simulation. • To make the workflow approachable for end users, we provide a MATLAB function that implements the SR(2) methodology.