scholarly journals Simulation study on the design of key technical parameters in marine environment sounding with fully polarimetric synthetic aperture radar based on ocean surface scattering model

2014 ◽  
Vol 63 (21) ◽  
pp. 218401
Author(s):  
Zhao Xian-Bin ◽  
Yan Wei ◽  
Wang Ying-Qiang ◽  
Lu Wen ◽  
Ma Shuo
Keyword(s):  
Synthetic Aperture Radar ◽  
Marine Environment ◽  
Simulation Study ◽  
Ocean Surface ◽  
Synthetic Aperture ◽  
Surface Scattering ◽  
Polarimetric Synthetic Aperture Radar ◽  
Scattering Model ◽  
Technical Parameters ◽  
Aperture Radar

scholarly journals TIME SERIES OF SALT CRUSTS IMAGED BY A DUAL POLARIZATION SPACEBORNE SYNTHETIC APERTURE RADAR (SAR) AT C-BAND OVER AN ANDEAN ALTIPLANO SALAR OF NORTHERN CHILE

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 83-88
Author(s):  
M. Barber ◽  
A. Delsouc ◽  
W. Perez ◽  
I. Briceño
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Crystal Surface ◽  
Snow Accumulation ◽  
Synthetic Aperture ◽  
Surface Scattering ◽  
Backscattering Coefficient ◽  
Standard Deviation Increase ◽  
Scattering Model ◽  
Aperture Radar

Abstract. A dense time series of Synthetic Aperture Radar acquisitions at 6-day intervals between July 2017 to January 2019 collected with the C-band constellation Sentinel 1A and 1B is used to study salt crust evolution in an highland salar. Microwave response of halite crystal aggregates is linked to surface roughness of the salt crusts by means of a surface scattering model which includes multiple scattering at second order in media with complex permittivity such as brine-soil mixtures. The time series enabled to estimate co-polarised vertical-vertical backscattering coefficient variations as large as 8.8 dB on a 4-month period which implied a height standard deviation increase from 0.5 to 4.5 mm as modeled by the surface scattering model. Backscattering coefficient variations between 0.8 to 2 dB per month are found for three different crusts, which demonstrated different growth rates of the crystals. Crystal growth rate might be driven by the kind of water input (rainfall or snow in Andean salars), probably due to the negative effect of water droplets on impinging halite crystal surface in comparison to snow. Results showed that cross-polarised backscattering coefficient is sensitive to snow accumulation and appeared to be sensitive to subsurface conditions.

scholarly journals Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5176
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Bingxin Liu ◽  
Peng Wu ◽  
Chen Chen
Keyword(s):  
Computational Complexity ◽  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Similarity Measures ◽  
Synthetic Aperture ◽  
Matching Method ◽  
Polarimetric Synthetic Aperture Radar ◽  
Spectral Similarity ◽  
Oil Spill Detection ◽  
Aperture Radar

Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

scholarly journals Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1607
Author(s):  
Guannan Li ◽  
Ying Li ◽  
Yongchao Hou ◽  
Xiang Wang ◽  
Lin Wang
Keyword(s):  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Data ◽  
Marine Oil ◽  
Oil Spill Detection ◽  
Oil Slick ◽  
Feature Parameters ◽  
Aperture Radar

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

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