A Novel At-Sensor Radiance Harmonization Method

Abstract

We present a novel harmonization method that we use to create an improved at-sensor radiance product (L1B) for imaging spectrometers. The unique feature of our approach is that in addition to the center wavelength and angle, the shapes of the spectral and geometric response functions of each single pixel can be individually modified. Our method can therefore be used to harmonize data between different instruments, but also to correct optical artifacts, such as keystone and smile. In this way, an L1B product can be created that resembles data from an (almost) ideal imaging spectrometer with minimal optical artifacts. For example, the output product can be designed to feature a uniform Gaussian response function with constant shape for all geometric pixels and spectral channels.

As usage of such a product requires less knowledge of the intricate optical properties of the imaging spectrometer used to obtain it, it significantly lowers the bar for inexperienced users to incorporate the product into their data evaluation chains. Additionally, it can reduce the computational burden on subsequent processing steps such as atmospheric correction or matched filter algorithms. Since artifacts removable by the proposed algorithm do not necessarily need to be corrected by optical engineering, our approach fosters cost-effective instrument design and production as overly strict (and often expensive) requirements on the optical instrument performance may be avoided.

The resulting L1B product is obtained by linear transformation of the radiometrically calibrated at-sensor radiance data. While it can be created for almost any sensor systems, the algorithm was designed with typical hyperspectral imaging spectrometers in mind. Its computation requires a comprehensive knowledge of an instrument’s spectral and geometric response functions and it tends to work best with sensors that oversample in the spectral and spatial directions.

We briefly summarize the mathematical prerequisites underlying the method before describing how we have implemented it to compute the L1B product for two airborne hyperspectral sensors (HySpex VNIR-3000N & SWIR-384) operated at the German Aerospace Center (DLR). Further, we describe the extensive on-ground system calibration performed in our laboratory to obtain the calibration key data required for the L1B product generation. We conclude with the presentation of exemplary L1B data, demonstrating that the algorithm works as expected and is capable of delivering an improved, high-quality and easy-to-use L1B product to the scientific community.

We believe that a similar L1B product could be a valuable extension to current and upcoming imaging spectrometer missions.