A data fusion approach for the analysis of azimuth ambiguities

Dellepiane, S.; Martino, Michaela De; Toma, Matteo

doi:10.1109/igarss.2013.6723744

Cited by 5 publications

(6 citation statements)

References 7 publications

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“…However, there has rarely been a study on RGB composition for azimuth ambiguities [11]. This section presents and compares two RGB composition methods for better azimuth ambiguities visualization based on the number of SAR images available.…”

Section: Rgb Composition Methods For Azimuth Ambiguitiesmentioning

confidence: 99%

“…The B channel is associated to a copy generated by the original image. The copy is the original image translated in an azimuth ambiguity displacement given by Formula (1) [11]. Similarly, azimuth ambiguities and strong source target pixels that are in good relationship appear in white.…”

Section: Only One Image Availablementioning

confidence: 99%

“…The Doppler shift as a function of a pointing error, increases linearly with frequency. Azimuth ambiguities in SAR images are spatially displaced in azimuth directions at approximate locations [11,14], as…”

Section: Review On Azimuth Ambiguitiesmentioning

confidence: 99%

“…SAR images often suffer from azimuth ambiguities. Experience shows that azimuth ambiguities are today a real problem for ocean monitoring [11,12]. SAR images can be strongly affected by land-based sources.…”

Section: Introductionmentioning

confidence: 99%

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Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Leng

Zhou

et al. 2017

Remote Sensing

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Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI). Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA). Both Interferometric Wide Swath (IW) and Stripmap (SM) mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively.

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Section: Rgb Composition Methods For Azimuth Ambiguitiesmentioning

confidence: 99%

Section: Only One Image Availablementioning

confidence: 99%

Section: Review On Azimuth Ambiguitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Leng

Zhou

et al. 2017

Remote Sensing

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“…This is one of the reasons why region boundary and target detection become very difficult tasks and efficient speckle filtering is often required [3]. Other possible distortions in SAR images of the sea are due to the scalloping effect and the presence of azimuth ambiguities [4] that must be appropriately faced. After the segmentation approaches based on intensity, edges, or texture [6], often in a contextual framework such that of Markov Random Fields (MRF) [7], more recent graph-based methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy graph-based segmentation for marine and maritime applications in SAR images

Gemme

Dellepiane

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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In the context of sea monitoring, an important processing step is image segmentation. In this paper, the authors propose a new segmentation method that combines fuzzy and graphbased theories. The algorithm, starting from a single source element belonging to the region of interest, proceeds with a propagation mechanism that aims at finding a Minimum Path Spanning Tree (MPST). The process is automatic, unsupervised, adaptive to the image content and independent from the order of analysis. A TerraSAR-X image and Cosmo-SkyMed images are used for the experiments. The considered applications are oil spill detection, sea surface analysis, and ship detection.

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The SEAGOSS project: Monitoring coastal seawater in Italy by remote sensing data

Martino

Dellepiane

Gemme

et al. 2014

2014 IEEE Geoscience and Remote Sensing Symposium

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A data fusion approach for the analysis of azimuth ambiguities

Cited by 5 publications

References 7 publications

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

A fuzzy graph-based segmentation for marine and maritime applications in SAR images

The SEAGOSS project: Monitoring coastal seawater in Italy by remote sensing data

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