15 October 2015 Striping noise mitigation: performance evaluation on real and simulated hyperspectral images
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Abstract
Striping noise is a phenomenon intrinsic to the process of image acquisition by means of scanning or pushbroom systems, caused by a poor radiometric calibration of the sensor. Although in-flight calibration has been performed, residual spatially and spectrally coherent noise may perturb the quantitative analysis of images and the extraction of physical parameters.

Destriping methods can be classified in three main groups: statistical-based methods, digital-filtering methods and radiometric-equalisation methods. Their performances depend both on the scene under investigation and on the type and intensity of noise to be treated. Availability of simulated data at each step of the digital image formation process, including that one before the introduction of the striping effect, is particularly useful since it offers the opportunity to test and adjust a variety of image processing and calibration algorithms.

This paper presents the performance of a statistical-based destriping method applied to a set of simulated and to images acquired by the EO-1 Hyperion hyperspectral sensor. The set of simulated data with different intensities of coherent and random noise was generated using an image simulator implemented for the PRISMA mission.

Algorithm’s performance was tested by evaluating most commonly used quality indexes. For the same purpose, a statistical evaluation based on image correlation and image differences between the corrected and ideal images was carried out. Results of the statistical analysis were compared with the outcome of the quality indexes-based analysis.
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Cinzia Lastri, Cinzia Lastri, Donatella Guzzi, Donatella Guzzi, Alessandro Barducci, Alessandro Barducci, Ivan Pippi, Ivan Pippi, Vanni Nardino, Vanni Nardino, Valentina Raimondi, Valentina Raimondi, } "Striping noise mitigation: performance evaluation on real and simulated hyperspectral images", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96430K (15 October 2015); doi: 10.1117/12.2195706; https://doi.org/10.1117/12.2195706
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