Scientific exploitation of PAZ products in coastal surveillance and monitoring tasks
Keywords:SAR, Speckle, Segmentation, Ship detection, Maritime applications
PAZ mission appears due to the need of a Spanish SAR satellite able to provide radar image products for security and defense, civil and scientific users. INTA is responsible for the technical direction of the Ground Segment, as well as the development of the Calibration and Validation Centre and the scientific exploitation. The ‘Demonstrator of Maritime SAR Applications’ is proposed as an answer to detection tasks in maritime synthetic aperture radar imagery, which are not completely solved yet. DeMSAR has been developed in the framework of a contract between the Spanish National Institute for Aerospace Technology (INTA) and the University of Alcalá. It is intended to be used as a demonstrator of the capabilities of the airborne SAR prototypes of INTA as well as for PAZ, the Spanish SAR satellite. With two operation modes, an automatic ship detector and a toolboxes mode, DeMSAR offers the user a high flexibility in SAR data processing tasks such as speckle filtering, coastline detection, land mask estimation and ship detection and characterization.
Canny, J., 1986. A computational approach to edge detection, IEEE Trans. on Pattern Anal. and Machine Intel., PAMI-8(6), 679-698. doi:10.1109/TPAMI.1986.4767851
Cheng, P. et al., 2005. Comparison of ship detection algorithms in spaceborne SAR imagery, IEEE IGARSS’05, 3, 1750-1752. doi:10.1109/IGARSS.2005.1526341
Comaniciu, D. & Meer, P., 2002. Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Anal. and Machine Intel., 24(5), 603-618. doi:10.1109/34.1000236
Duda, R. O. & Hart, P. E., 1973. Pattern classification and scene analysis. Wiley.
Frost, V. S., Stiles, J. A., Shanmugan, K. S. & Holtzman, J. C., 1982. A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Trans. on Pattern Anal. and Machine Intel., PAMI-4(2), 157-166. doi:10.1109/TPAMI.1982.4767223
Fukunaga, K. & Hostetler, L., 1975. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 21(1), 32-40. doi:10.1109/TIT.1975.1055330
Jarabo, P., Rosa, M., De la Mata, D., Vicen, R. & Maldonado, S., 2011. Spatial-Range Mean-Shift Filtering and Segmentation Applied to SAR Images, IEEE Trans. on Instrum. and Measurement. 60(2), 584-597. doi:10.1109/TIM.2010.2052478
Lee, J. S., 1980. Digital Image Enhancement and noise filtering by use of local statistics, IEEE Trans. on Pattern Anal. and Machine Intel., PAMI-2(2), 165-168. doi:10.1109/TPAMI.1980.4766994
Lopes, A., Nezry, E., Touzi, R. & Laur, H., 1990. Maximum a posteriori filtering and first order texture models in SAR images, IGARSS’90, Washington D.C., Estados Unidos, 2409-2412. doi:10.1109/IGARSS.1990.689026
Mallat, S., 2008. A wavelet tour of signal processing. 3rd Edition. Academic Press.
Mata-Moya, D. et al., 2010. Application of Mean-Shift filtering to ship wakes detection in SAR images, European Conference on Synthetic Aperture Radar, Aachen, Alemania.
Niedermeier, A. et al., 2000. Detection of coastlines in SAR images using wavelet methods, IEEE Trans. on Geosc. Remote Sensing, 38(5), 2270-2281. doi:10.1109/36.868884
Touzi, R., Lopes, A. & Bousquet, P., 1988. A statistical and geometrical edge detector for SAR images, IEEE Transactions on Geoscience and Remote Sensing, 26, 764-773. doi:10.1109/36.7708
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