Analysis of Adaptive and Advanced Speckle Filters on SAR Data

Misra, Arundhati; Ajmera, Dhwani

doi:10.9790/0661-1901024854

Cited by 2 publications

(3 citation statements)

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“…However, average filtering is expected to be a good choice in any situation where no subtle structures such as dihedral or trihedral corner reflectors or other textural information need to be preserved. Indeed, one of the quality indices for speckle filters is the mean preservation index (see [24]). In [24], many classical and some advanced speckle filters are investigated, and it is found that at least the classical filters preserve the mean very well, which in turn means that in homogeneous areas they more or less act as an averaging filter.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, one of the quality indices for speckle filters is the mean preservation index (see [24]). In [24], many classical and some advanced speckle filters are investigated, and it is found that at least the classical filters preserve the mean very well, which in turn means that in homogeneous areas they more or less act as an averaging filter.…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, many speckle filters exist, see e.g., [23,24] for overviews of the classical and some more advanced speckle filters, and [25,26] for more recent developments involving convolutional neural networks. Several of these were applied to the original amplitude images A 0,1 and A 0,2 to evaluate their impact on the enhancement of the contrast between the high-coherence and the low-coherence parts of the scene.…”

Section: Speckle Filteringmentioning

confidence: 99%

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Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images

2021

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In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%