Directional Smoothing Algorithm for Edge-Preserving Smoothing of SAR Images

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Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images

Mario Mastriani, Alberto E. Giraldez

PDF: Enhanced Directional Smoothing Algorithm for Edge-Preserving Smoothing of Synthetic-Aperture Radar Images

Synthetic aperture radar (SAR) imaging of the earth’s surface is a valuable modality for remote sensing in Argentina, since SAR is able to penetrate cloud cover and is independent of solar illumination. However, speckle noise generated from the coherent imaging technique of SAR is a serious impediment to computer interpretation of SAR images. This speckle noise can be successfully modeled as a purely multiplicative noise process, and as a result several interesting properties of the noise can be exploited to help reduce the noise without blurring or distorting edges . In theory, the ratio of the standard deviation to the signal value, the “coefficient of variation,” is constant at every point in an image corrupted by purely multiplicative noise [1]. This property is not true in all the possible used images. We use a new filter structure independent of such property which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR images without blurring edges. The new filter structure is able to direct a filtering operation to act over the complete image. By directing the smoothing operation away from edges, the filter reduces noise while sharpening edges. Methods used previously to reduce noise in images include speckle filters such as Median, Lee, Kuan, Frost, enhanced Lee, enhanced Frost, Gamma or MAP, morphology-based nonlinear filter, and DS [15,16]. Another possibility is de-noising a SAR image via wavelet shrinkage with a considerable computational complexity, based on wavelets properties.

Synthetic aperture radar (SAR) images are subject to prominent speckle noise, which is generally considered a purely multiplicative noise process. In theory, this multiplicative noise is that the ratio of the standard deviation to the signal value, the “coefficient of variation,” is theoretically constant at every point in a SAR image. Most of the filters for speckle reduction are based on this property. Such property is irrelevant for the new filter structure, which is based on directional smoothing (DS) theory, the enhanced directional smoothing (EDS) that removes speckle noise from SAR images without blurring edges. We demonstrate the effectiveness of this new filtering method by comparing it to established speckle noise removal techniques on SAR images.

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