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

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.

[1]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[2]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[3]  Dan Garcia,et al.  A novel adaptive synthetic aperture radar ship detection system , 2011, OCEANS'11 MTS/IEEE KONA.

[4]  Huanxin Zou,et al.  A Bilateral CFAR Algorithm for Ship Detection in SAR Images , 2015, IEEE Geoscience and Remote Sensing Letters.

[5]  Chen Liu,et al.  A New Application for PolSAR Imagery in the Field of Moving Target Indication/Ship Detection , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[7]  Naouma Kourti,et al.  The SUMO Ship Detector Algorithm for Satellite Radar Images , 2017, Remote. Sens..

[8]  Harm Greidanus,et al.  SAR Image Quality Assessment and Indicators for Vessel and Oil Spill Detection , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[9]  David James Crisp.  A ship detection system for RADARSAT-2 dual-pol multi-look imagery implemented in the ADSS , 2013, 2013 International Conference on Radar.

[10]  Thomas Fritz,et al.  Ship Surveillance With TerraSAR-X , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Silvana G. Dellepiane,et al.  A New Method for Cross-Normalization and Multitemporal Visualization of SAR Images for the Detection of Flooded Areas , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Marco Martorella,et al.  Spaceborne Radar Imaging of Maritime Moving Targets With the Cosmo-SkyMed SAR System , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  D. Crisp,et al.  The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery , 2004 .

[14]  Luigi Fusco,et al.  Near real-time SAR-based processing to support flood monitoring , 2009, Journal of Real-Time Image Processing.

[15]  Andrea Monti Guarnieri,et al.  Adaptive removal of azimuth ambiguities in SAR images , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Andreas Niedermeier,et al.  Detection of coastlines in SAR images using wavelet methods , 2000, IEEE Trans. Geosci. Remote. Sens..

[17]  Mingsheng Liao,et al.  Removal of azimuth ambiguities and detection of a ship: using polarimetric airborne C-band SAR images , 2012 .

[18]  Huanxin Zou,et al.  An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery , 2016, Sensors.

[19]  Van Wimersma Greidanus Herman,et al.  First Analyses of Sentinel-1 Images for Maritime Surveillance , 2014 .

[20]  Domenico Velotto,et al.  Azimuth ambiguities removal for ship detection using full polarimetric X-band SAR data , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[21]  John C. Curlander,et al.  Synthetic Aperture Radar: Systems and Signal Processing , 1991 .

[22]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[23]  Giampaolo Ferraioli,et al.  Unsupervised Coastal Line Extraction From SAR Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[24]  Silvana G. Dellepiane,et al.  A data fusion approach for the analysis of azimuth ambiguities , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[25]  Antonio Iodice,et al.  Filtering of Azimuth Ambiguity in Stripmap Synthetic Aperture Radar Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.