Advances in reducing radiometric miscalibration - application for hyperspectral push-broom sensors

Data takes of hyperspectral imagers are of increasing demand in Earth Observation related applications. As for other remote sensing techniques this requires precise pre-processing comprising of radiometric, spectral and geometric distortion reductions. One of these steps is radiometric scaling to transform recorded digital number to radiance. For this, laboratory assessed mathematical relations between required radiance and recorded digital number (gain) and short-term measurements of dark current variations (offset) during operation are incorporated. Due to changes in the sensor system, which include thermal imbalance and mechanical stress gain and offset may vary over time. The result of this is visually perceptible as along-track striping noise after radiometric calibration. In this work, a new approach is presented that enables fast, highly precise and parameter-free destriping of uncorrelated striping noise that enhances the radiometric accuracy of hyperspectral push-broom data takes, and, hence, improves the outputs of succeeding applications. It is part of the existing ROME (Reduction of Miscalibration Effects) framework and is based on a noise-perpendicular gradient minimization technique. The performance was tested and compared to four state-of-the-art algorithms using artificially degraded hyperspectral whisk-broom scenes from a HyMAP campaign over Germany, two AISA scenes over Germany and two EO-1 Hyperion scenes over Namibia. Proposed approach clearly outperforms all other tested approaches even in low SNR scenarios like close to atmospheric absorption bands. On average a destriping accuracy of 99.75 % can be achieved having 3σ of only 1 % and, thus, it has been integrated into the state-of-the-art ROME framework that becomes a standard inside hyperspectral pre-processing chains.