Sea State Primitive Object Creation from SAR Data
Affiliation(s)
Department of Marine Sciences, School of the Environment, University of the Aegean, Mytilene, Greece.
Department of Marine Sciences, School of the Environment, University of the Aegean, Mytilene, Greece.
ABSTRACT
Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surface or internal waves, eddies, oceanic fronts. One of most challenging processing step is to create image objects describing these phenomena on SAR images. The most significant problem in the wide swath images is the backscattering trend at the range direction, which results a progressive brightness reduction over images from near to far range. This reduction affects the detection and classification of sea surface features on wide swath SAR images and a normalization step is needed in a certain incidence angle for compensating the brightness reduction. The aim of the present paper is to investigate the result of image normalization to a set of Wide Swath Mode SAR images. Dark areas were initially detected in SAR images using thresholds, adapted or not. Afterwards, SAR images were normalized and a global threshold was calculated for each image. Images were segmented and objects were created for each dark area. The results were compared to a reference dataset created from theoretical modeled values and extracted in a GIS environment. Results clearly indicate that overall accuracy of the detected dark areas has been increased after normalization. On the contrary, local thresholds were insufficient in producing acceptable results. The proposed normalization can be used as a pre-processing step in image classification.
Wide swath Synthetic Aperture Radar (SAR) images acquired over sea areas contain a variety of information regarding small scale and mesoscale phenomena in the ocean and marine boundary layer e.g. spills, slicks, surface or internal waves, eddies, oceanic fronts. One of most challenging processing step is to create image objects describing these phenomena on SAR images. The most significant problem in the wide swath images is the backscattering trend at the range direction, which results a progressive brightness reduction over images from near to far range. This reduction affects the detection and classification of sea surface features on wide swath SAR images and a normalization step is needed in a certain incidence angle for compensating the brightness reduction. The aim of the present paper is to investigate the result of image normalization to a set of Wide Swath Mode SAR images. Dark areas were initially detected in SAR images using thresholds, adapted or not. Afterwards, SAR images were normalized and a global threshold was calculated for each image. Images were segmented and objects were created for each dark area. The results were compared to a reference dataset created from theoretical modeled values and extracted in a GIS environment. Results clearly indicate that overall accuracy of the detected dark areas has been increased after normalization. On the contrary, local thresholds were insufficient in producing acceptable results. The proposed normalization can be used as a pre-processing step in image classification.
Cite this paper
Topouzelis, K. and Kitsiou, D. (2014) Sea State Primitive Object Creation from SAR Data. International Journal of Geosciences, 5, 1561-1570. doi: 10.4236/ijg.2014.513127.
Topouzelis, K. and Kitsiou, D. (2014) Sea State Primitive Object Creation from SAR Data. International Journal of Geosciences, 5, 1561-1570. doi: 10.4236/ijg.2014.513127.
References
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http://dx.doi.org/10.1080/014311600750037589 [2] Nirchio, F., Sorgente, M., Giancaspro, A., Biamino, W., Parisato, E., Ravera, R., et al. (2005) Automatic Detection of Oil Spills from SAR Images. International Journal of Remote Sensing, 26, 1157-1174.
http://dx.doi.org/10.1080/01431160512331326558 [3] Solberg, A., Brekke, C. and Husoy, P.O. (2007) Oil Spill Detection in Radarsat and Envisat SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 45, 746-755.
http://dx.doi.org/10.1109/TGRS.2006.887019 [4] Karathanassi, V., Topouzelis, K., Pavlakis, P. and Rokos, D. (2006) An Object-Oriented Methodology to Detect Oil Spills. International Journal of Remote Sensing, 27, 5235-5251.
http://dx.doi.org/10.1080/01431160600693575 [5] Del Frate, F., Petrocchi, A., Lichtenegger, J. and Calabresi, G. (2000) Neural Networks for Oil Spill Detection Using ERS-SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 38, 2282-2287.
http://dx.doi.org/10.1109/36.868885 [6] Liu, A.K., Peng, C.Y. and Chang, S.Y.-S. (1997) Wavelet Analysis of Satellite Images for Coastal Watch. IEEE Journal of Oceanic Engineering, 22, 9-17.
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http://dx.doi.org/10.1016/j.patcog.2006.04.032 [8] Benelli, G. and Garzelli, A. (1999) Oil-Spills Detection in SAR Images by Fractal Dimension Estimation. IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS’99 (Cat. No.99CH36293), 1, 218-220.
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http://dx.doi.org/10.1016/S0303-2434(01)85011-X [10] Topouzelis, K.N. (2008) Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms. Sensors, 8, 6642-6659.
http://dx.doi.org/10.3390/s8106642 [11] Singha, S., Vespe, M. and Trieschmann, O. (2013) Automatic Synthetic Aperture Radar Based Oil Spill Detection and Performance Estimation via a Semi-Automatic Operational Service Benchmark. Marine Pollution Bulletin, 73, 199-209.
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http://dx.doi.org/10.1175/JPO3028.1 [13] Nittis, K. and Perivoliotis, L. (2002) Circulation and Hydrological Characteristics of the North Aegean Sea: A Contribution from Real-Time Buoy Measurements. Mediterranean Marine Science, 3, 21-32.
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http://dx.doi.org/10.1029/RS021i005p00845 [20] Congalton, R. and Green, K. (2008) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Second Edition, CRC Press, Boca Raton. [21] Hersbach, H., Stoffelen, A. and De Haan, S. (2003) CMOD5: An Improved Geophysical Model Function for ERS C-Band Scatterometry. ECMWF Technical Memorandum No. 395, 1-52.
[1] Fiscella, B., Giancaspro, A., Nirchio, F., Pavese, P. and Trivero, P. (2000) Oil Spill Detection Using Marine SAR Images. International Journal of Remote Sensing, 21, 3561-3566.
http://dx.doi.org/10.1080/014311600750037589 [2] Nirchio, F., Sorgente, M., Giancaspro, A., Biamino, W., Parisato, E., Ravera, R., et al. (2005) Automatic Detection of Oil Spills from SAR Images. International Journal of Remote Sensing, 26, 1157-1174.
http://dx.doi.org/10.1080/01431160512331326558 [3] Solberg, A., Brekke, C. and Husoy, P.O. (2007) Oil Spill Detection in Radarsat and Envisat SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 45, 746-755.
http://dx.doi.org/10.1109/TGRS.2006.887019 [4] Karathanassi, V., Topouzelis, K., Pavlakis, P. and Rokos, D. (2006) An Object-Oriented Methodology to Detect Oil Spills. International Journal of Remote Sensing, 27, 5235-5251.
http://dx.doi.org/10.1080/01431160600693575 [5] Del Frate, F., Petrocchi, A., Lichtenegger, J. and Calabresi, G. (2000) Neural Networks for Oil Spill Detection Using ERS-SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 38, 2282-2287.
http://dx.doi.org/10.1109/36.868885 [6] Liu, A.K., Peng, C.Y. and Chang, S.Y.-S. (1997) Wavelet Analysis of Satellite Images for Coastal Watch. IEEE Journal of Oceanic Engineering, 22, 9-17.
http://dx.doi.org/10.1109/48.557535 [7] Derrode, S. and Mercier, G. (2007) Unsupervised Multiscale Oil Slick Segmentation from SAR Images Using a Vector HMC Model. Pattern Recognition, 40, 1135-1147.
http://dx.doi.org/10.1016/j.patcog.2006.04.032 [8] Benelli, G. and Garzelli, A. (1999) Oil-Spills Detection in SAR Images by Fractal Dimension Estimation. IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS’99 (Cat. No.99CH36293), 1, 218-220.
http://dx.doi.org/10.1109/IGARSS.1999.773452 [9] Marghany, M. (2001) RADARSAT Automatic Algorithms for Detecting Coastal Oil Spill Pollution. International Journal of Applied Earth Observation and Geoinformation, 3, 191-196.
http://dx.doi.org/10.1016/S0303-2434(01)85011-X [10] Topouzelis, K.N. (2008) Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms. Sensors, 8, 6642-6659.
http://dx.doi.org/10.3390/s8106642 [11] Singha, S., Vespe, M. and Trieschmann, O. (2013) Automatic Synthetic Aperture Radar Based Oil Spill Detection and Performance Estimation via a Semi-Automatic Operational Service Benchmark. Marine Pollution Bulletin, 73, 199-209.
http://dx.doi.org/10.1016/j.marpolbul.2013.05.022 [12] Olson, D.B., Kourafalou, V.H., Johns, W.E., Samuels, G. and Veneziani, M. (2007) Aegean Surface Circulation from a Satellite-Tracked Drifter Array. Journal of Physical Oceanography, 37, 1898-1917.
http://dx.doi.org/10.1175/JPO3028.1 [13] Nittis, K. and Perivoliotis, L. (2002) Circulation and Hydrological Characteristics of the North Aegean Sea: A Contribution from Real-Time Buoy Measurements. Mediterranean Marine Science, 3, 21-32.
http://dx.doi.org/10.12681/mms.255 [14] Brekke, C. and Solberg, A. (2005) Oil Spill Detection by Satellite Remote Sensing. Remote Sensing of Environment, 95, 1-13.
http://dx.doi.org/10.1016/j.rse.2004.11.015 [15] Desnos, Y.-L., Laur, H., Closa, J. and Meisl, P. (2000) The Envisat ASAR Processor and Data Products, SAR Work. Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation, 450. [16] Rosich, B. and Meadows, P. (2004) Absolute Calibration of ASAR Level 1 Products, 26. [17] Ulaby, F.T., Moore, R.K. and Fung, A.K. (1982) Microwave Remote Sensing: Active and Passive Volume II: Radar Remote Sensing and Surface Scattering and Emission Theory. Artech House. [18] Wessel, P. and Smith, W.H.F. (1996) A Global, Self-Consistent, Hierarchical, High-Resolution Shoreline Database. Journal of Geophysical Research, 101, 8741-8743.
http://dx.doi.org/10.1029/96JB00104 [19] Feindt, F., Wismann, V., Alpers, W. and Keller, W.C. (1986) Airborne Measurements of the Ocean Radar Cross Section at 5.3 GHz as a Function of Wind Speed. Radio Science, 21, 845-856.
http://dx.doi.org/10.1029/RS021i005p00845 [20] Congalton, R. and Green, K. (2008) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Second Edition, CRC Press, Boca Raton. [21] Hersbach, H., Stoffelen, A. and De Haan, S. (2003) CMOD5: An Improved Geophysical Model Function for ERS C-Band Scatterometry. ECMWF Technical Memorandum No. 395, 1-52.