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https://www.um.edu.mt/library/oar/handle/123456789/69697
Title: | Efficient destriping of remote sensing images using an oriented super-Gaussian filter |
Authors: | Lloyd, David Bouali, Marouan Abela, Aaron Farrugia, Reuben A. Valentino, Gianluca |
Keywords: | Remote sensing Image processing |
Issue Date: | 2020-09 |
Publisher: | International Society for Optics and Photonics |
Citation: | Lloyd, D. T., Bouali, M., Abela, A., Farrugia, R., & Valentino, G. (2020, September). Efficient destriping of remote sensing images using an oriented super-Gaussian filter. In Image and Signal Processing for Remote Sensing XXVI (Vol. 11533, p. 1153306). International Society for Optics and Photonics. |
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. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/69697 |
Appears in Collections: | Scholarly Works - FacICTCCE |
Files in This Item:
File | Description | Size | Format | |
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1153306.pdf Restricted Access | 24.51 MB | Adobe PDF | View/Open Request a copy |
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