2017
DOI: 10.3390/rs9080866
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Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images

Abstract: 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-tempo… Show more

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Cited by 13 publications
(2 citation statements)
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“…Finally, a different approach exploiting the combination of the two cross-polarized channels has been proposed to improve the detection performance. However, the application of polarimetric technique is strongly limited by the availability and coverage of the relevant data [17]. Recent studies have detected ships using deep learning techniques, such as Convolutional Neural Networks (CNNs) for image-based feature extraction [18][19][20][21][22].…”
Section: Of 16mentioning
confidence: 99%
“…Finally, a different approach exploiting the combination of the two cross-polarized channels has been proposed to improve the detection performance. However, the application of polarimetric technique is strongly limited by the availability and coverage of the relevant data [17]. Recent studies have detected ships using deep learning techniques, such as Convolutional Neural Networks (CNNs) for image-based feature extraction [18][19][20][21][22].…”
Section: Of 16mentioning
confidence: 99%
“…These are in part due to features such as strong sea clutter, wave crests, range ambiguities, or unmasked land [5,7,18]. Systematic azimuth ambiguities caused by strong land-based scatters could be further removed before applying the classifier, by implementing multi-temporal methods as suggested in [7,29]. Radio Frequency Interference (RFI) [7,30] is another potential source of (non-systematic) false detections.…”
Section: Vessel Detection With Sumo and Sentinel-1mentioning
confidence: 99%