A Radon-Transform-Based Image Noise Filter - With Applications to Multibeam Bathymetry

Author
Landmark, Knut
Solberg, Anne H Schistad
Albregtsen, Fritz
Austeng, Andreas
Hansen, Roy Edgar
Date Issued
2015
Keywords
Batymetri
Sonar
Permalink
https://ffi-publikasjoner.archive.knowledgearc.net/handle/20.500.12242/489
DOI
10.1109/TGRS.2015.2436380
Collection
Articles
Description
Landmark, Knut; Solberg, Anne H Schistad; Albregtsen, Fritz; Austeng, Andreas; Hansen, Roy Edgar. A Radon-Transform-Based Image Noise Filter - With Applications to Multibeam Bathymetry. IEEE Transactions on Geoscience and Remote Sensing 2015 ;Volum 53.(11) s. 6252-6273
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Abstract
This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Götz, Druckmüller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10-28% for two sets of invariant terrain features.
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