Efficient destriping of remote sensing images using an oriented super-Gaussian filter

David T. Lloyd; Marouan Bouali; Aaron Abela; Reuben Farrugia; Gianluca Valentino

doi:10.1117/12.2574449

20 September 2020 Efficient destriping of remote sensing images using an oriented super-Gaussian filter

David T. Lloyd, Marouan Bouali, Aaron Abela, Reuben Farrugia, Gianluca Valentino

Proceedings Volume 11533, Image and Signal Processing for Remote Sensing XXVI; 1153306 (2020) https://doi.org/10.1117/12.2574449
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

Satellite imagery provides information crucial for remote sensing applications. However, the images themselves can suffer from systematic and random artefacts which reduce the utility and accuracy of datasets. In particular, radiometric miscalibration due to temporal variation of the detector response may result in stripe noise. We report a method for suppressing striping in remote sensing images by use of a Fourier filter shaped like a superGaussian function. In comparison to both established ‘traditional’ and deep-learning-based destriping techniques, our method demonstrates superior destriping performance for both remote sensing images with native striping as well as those with stripes added to them. Our method simultaneously meets the three criteria of fidelity, speed and flexibility, enabling an efficient improvement in the radiometric accuracy of images from a wide range of satellite sources.

Conference Presentation

Citation Download Citation

David T. Lloyd, Marouan Bouali, Aaron Abela, Reuben Farrugia, and Gianluca Valentino "Efficient destriping of remote sensing images using an oriented super-Gaussian filter", Proc. SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, 1153306 (20 September 2020); https://doi.org/10.1117/12.2574449

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