RCM Polarimetric SAR for Enhanced Ship Detection and Classification

Touzi, R.; Vachon, P.W.

doi:10.1080/07038992.2015.1110010

Cited by 26 publications

(11 citation statements)

References 32 publications

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“…Shirvany et al [11] indicated the effectiveness of the degree of polarization (DoP) in ship detection. Then, this work was further studied by Touzi et al [12], who defined an extension of the DoP to enhance significant ship-sea contrasts. In contrast to using a single feature, Yin et al [13] investigated the capability of m-α and m-χ decompositions in coastal ship detection.…”

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confidence: 99%

Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR

et al. 2019

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Polarimetric synthetic aperture radar (SAR) is currently drawing more attention due to its advantage in Earth observations, especially in ship detection. In order to establish a reliable feature selection method for marine vessel monitoring purposes, forty features are extracted via polarimetric decomposition in the full-polarimetric (FP), compact-polarimetric (CP), and dual-polarimetric (DP) modes. These features were comprehensively quantified and evaluated using the Euclidean distance and mutual information, and the result indicated that the features in CP SAR are better than those of FP or DP SAR in general. The CP SAR features are thus further studied, and a new feature, named phase factor, in CP SAR mode is presented that can distinguish ships and the sea surface by the constant 0 without complex calculation. Furthermore, the phase factor is independent of the sea surface roughness, and hence it performs stably for ship detection even in high sea states. Experiments demonstrated that the ship detection performance of the phase factor detector is better than that of roundness, delta, HESA and CFAR detectors in low, medium and high sea states.2 of 23 much better. Qin et al. [3] proposed a novel CFAR detection algorithm for high-resolution SAR images using the generalized Gamma distribution (GΓD), and the performance of the proposed algorithm is better than the Weibull distribution. However, with the higher resolution of the SAR image, the sea clutter becomes complex in the time and spatial domains, and then the existing models are not suitable, resulting in the severe degradation of the CFAR detection performance and many false alarms [4]. Additionally, the parameter estimation is complex, and the threshold cannot be acquired easily [5].To overcome the drawbacks of the CFAR method, ship detection methods based on new features have been studied by researchers, and many results has been achieved [6][7][8]. For example, based on Cloude decomposition, Wang et al. [9] used the local uniformity of the third eigenvalue of a polarization coherence matrix (T) to detect ships. Sugimoto et al. [10] combined Yamaguchi decomposition theory and the CFAR method to detect ships by analyzing the differences between the scattering mechanisms of the sea surface and ships. Shirvany et al. [11] indicated the effectiveness of the degree of polarization (DoP) in ship detection. Then, this work was further studied by Touzi et al. [12], who defined an extension of the DoP to enhance significant ship-sea contrasts. In contrast to using a single feature, Yin et al. [13] investigated the capability of m-α and m-χ decompositions in coastal ship detection. Then, three features extracted from compact-polarimetric (CP) SAR were proven to have a good performance in ship detection in [14]. Furthermore, Paes et al. [15] provided a more detailed analysis of the detection capability and sensitivity of δ together with m, µ c ,|µ xy |, and the entropy H ω . Gui et al. [16] extracted a new feature from the proposed power-entropy decomposition, call...

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Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR

et al. 2019

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“…Both the PNF and PWF are based on the polarimetric covariance matrix, but Touzi et al showed that the covariance matrix and the tools based on it such as PNF and PWF detectors cannot cover the full polarimetric information required for enhanced ship detection [20]. Touzi et al also showed that excursion of the degree of polarization provide important information that is not exploited by the covariance matrix as well as the Marino detector [21]. The degree of polarization provides very important information that is not covered by conventional covariance matrix optimization tools such as the Notch detector.…”

Section: A Review Of Ship Detection In Polsar Imagerymentioning

confidence: 99%

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Liu

Yang

Marino

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Constant false alarm rate (CFAR) algorithms using local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this paper a polarimetric truncated statistic CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for Pol SAR imagery. The CFAR detector uses polarimetric whitening filter (PWF) to turn the multidimensional problem to a one dimensional case. It uses the truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require a prior knowledge of the interfering targets and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations and the results are in line with the theory.

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“…Such radars are capable of penetrating cloud cover, day or nighttime data collection, and capturing the physical and dielectric information of the ground objects. SAR data have proven to be useful in national wetland studies [6], natural disaster monitoring and mitigation [7], oil spill detection [8,9], and ship and sea-ice monitoring [10][11][12], especially in northern latitudes [13]. In addition, SAR data commonly have been used in conjunction with optical satellite data to better survey and characterize the Earth's surface [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission

Brisco¹,

Mahdianpari

Mohammadimanesh

2020

Remote Sensing

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Canada’s successful space-based earth-observation (EO) radar program has earned widespread and expanding user acceptance following the launch of RADARSAT-1 in 1995. RADARSAT-2, launched in 2007, while providing data continuity for its predecessor’s imaging capabilities, added new polarimetric modes. Canada’s follow-up program, the RADARSAT Constellation Mission (RCM), launched in 2019, while providing continuity for its two predecessors, includes an innovative suite of polarimetric modes. In an effort to make polarimetry accessible to a wide range of operational users, RCM uses a new method called hybrid compact polarization (HCP). There are two essential elements to this approach: (1) transmit only one polarization, circular; and (2) receive two orthogonal polarizations, for which RCM uses H and V. This configuration overcomes the conventional dual and full polarimetric system limitations, which are lacking enough polarimetric information and having a small swath width, respectively. Thus, HCP data can be considered as dual-pol data, while the resulting polarimetric classifications of features in an observed scene are of comparable accuracy as those derived from the traditional fully polarimetric (FP) approach. At the same time, RCM’s HCP methodology is applicable to all imaging modes, including wide swath and ScanSAR, thus overcoming critical limitations of traditional imaging radar polarimetry for operational use. The primary image data products from an HCP radar are different from those of a traditional polarimetric radar. Because the HCP modes transmit circularly polarized signals, the data processing to extract polarimetric information requires different approaches than those used for conventional linearly polarized polarimetric data. Operational users, as well as researchers and students, are most likely to achieve disappointing results if they work with traditional polarimetric processing tools. New tools are required. Existing tutorials, older seminar notes, and reference papers are not sufficient, and if left unrevised, could succeed in discouraging further use of RCM polarimetric data. This paper is designed to provide an initial response to that need. A systematic review of studies that used HCP SAR data for environmental monitoring is also provided. Based on this review, HCP SAR data have been employed in oil spill monitoring, target detection, sea ice monitoring, agriculture, wetland classification, and other land cover applications.

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RCM Polarimetric SAR for Enhanced Ship Detection and Classification

Cited by 26 publications

References 32 publications

Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR

Analysis of Ship Detection Performance with Full-, Compact- and Dual-Polarimetric SAR

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Hybrid Compact Polarimetric SAR for Environmental Monitoring with the RADARSAT Constellation Mission

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