Azimuth Ambiguity Suppression for Hybrid Polarimetric Synthetic Aperture Radar via Waveform Diversity

Hybrid quadrature polarimetric (hybrid quad-pol) synthetic aperture radar (SAR) is proposed as a potential candidate for the full-polarimetric SAR mode. It allows balanced range ambiguity performance and simplified system structure. System based on hybrid-pol SAR mode can also implement the conventional quad-pol mode and the compact-pol mode via few adjustments. However, the azimuth ambiguity performance in cross-pol channels is proved deteriorated in hybrid quad-pol mode due to the lopsided energy distribution of ambiguities. As are generally called “ghost” targets, azimuth ambiguities usually influence the recognition of the targets in SAR imaging. This letter describes how to remove the false targets that arise from azimuth ambiguities by means of waveform diversity and dual-focus post-processing (DFPP) technique. The proposed method exploits the feature of azimuth ambiguity and yields improved image quality in cross-pol channels with strong co-pol azimuth ambiguities removed in hybrid quad-pol SAR at a low system cost. Furthermore, it offers remarkable benefits for target detecting and recognition with strong false targets removed.

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

Sensors ◽

10.3390/s19235176 ◽

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.

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Fully polarimetric synthetic aperture radar data classification using probabilistic and non-probabilistic kernel methods

European Journal of Remote Sensing ◽

10.1080/22797254.2021.1924081 ◽

2021 ◽

pp. 1-8

Author(s):

Iman Khosravi ◽

Yury Razoumny ◽

Javad Hatami Afkoueieh ◽

Seyed Kazem Alavipanah

Keyword(s):

Synthetic Aperture Radar ◽

Kernel Methods ◽

Data Classification ◽

Radar Data ◽

Synthetic Aperture ◽

Polarimetric Synthetic Aperture Radar ◽

Synthetic Aperture Radar Data ◽

Aperture Radar

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

Remote Sensing ◽

10.3390/rs13091607 ◽

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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