Journal ArticleDOI
A Radon-Transform-Based Image Noise Filter—With Applications to Multibeam Bathymetry
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TLDR
Denoising for multibeam bathymetry data sets had less noise and distortion compared with those obtained with standard low-pass filters, and improved the accuracy in statistical classification of geomorphological type by 10-28% for two sets of invariant terrain features.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 Gotz, Druckmuller, 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.read more
Citations
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Journal ArticleDOI
VHR coastal bathymetry using WorldView-3: colour versus learner
TL;DR: In this paper, a case study over Saint-Malo (Brittany, France) megatidal turbid waters enables to quantify the accuracy gain related to Coastal and yellow bands as well as ordinary least squares (OLS), generalized linear model (GLM), and artificial neural network (ANN).
Journal ArticleDOI
Artefacts in Marine Digital Terrain Models: A Multiscale Analysis of Their Impact on the Derivation of Terrain Attributes
TL;DR: The contribution of different types of artefacts to marine terrain attributes at multiple scales is described and results indicate that most artefacts impact spatial similarity and that pitch and roll significantly impact the statistical distribution of DBMs and terrain attributes while time and heave artefacts have a more subtle impact.
Journal ArticleDOI
Detection and RM correction approach for manoeuvring target with complex motions
TL;DR: This study addresses the coherent accumulation problem for detecting a manoeuvring target with complex motions, where range migration (RM) and range curvature (RC) occur during the coherent integration time and an efficient approach based on generalised keystone transform, radon transform and generalised dechirp process is presented.
Journal ArticleDOI
Modified Radon transform inversion using moments
TL;DR: In this article, a modified Radon transform (MRT) via convolution with a mollifier and obtaining its inversion formula is derived, and a simple density function is reconstructed from the moments of its modified radon transform, which is used to provide a uniform approximation to the original density function.
Proceedings ArticleDOI
Tilt correction method of text image based on wavelet pyramid
Mingyang Yu,Qiguo Zhu +1 more
TL;DR: The experimental result shows this method can correct text images accurately and calculates the intersection of straight lines and gets the corrected text images according to the intersection points and perspective transformation.
References
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