PolSAR Ship Detection Based on Polarimetric Correlation Pattern

Cui, Xing-Chao; Tao, Chen-Song; Su, Yi; Chen, Siwei

doi:10.1109/lgrs.2020.2976477

Cited by 26 publications

(13 citation statements)

References 19 publications

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“…The SO-CFAR method [6] and superpixel method [18] are selected for comparison. In addition, in order to verify the advantages of the optimal polarimetric rotation domain features, T diagonal elements (T 11 , T | γHH-HV (θ)| org ) with high target-to-clutter ratio (TCR) [30] are also used for comparison. These methods are named SO-CFAR, SP, T3 + SVM, and PCP + SVM in the following, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The correlation values of the two polarization channels also contain potential information [9]. Based on the polarimetric coherence pattern, Cui et al [30] proposed the polarimetric correlation pattern for ship detection, which achieved good ship detection results. For arbitrary two polarization channels s 1 and s 2 , the polarimetric correlation pattern can be written as:…”

Section: Polarimetric Correlation Patternmentioning

confidence: 99%

“…However, rich information is also contained within this target scattering diversity [28]. Recently, the uniform polarimetric matrix rotation theory [28], polarimetric coherence, and correlation pattern techniques [29,30] were developed to explore the hidden information in the polarimetric rotation domain. These techniques have achieved many successful applications [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the uniform polarimetric matrix rotation theory [28], polarimetric coherence, and correlation pattern techniques [29,30] were developed to explore the hidden information in the polarimetric rotation domain. These techniques have achieved many successful applications [30][31][32]. This work focuses on the hidden feature exploration of polarimetric coherence and correlation pattern and the optimal combination of these features for ship detection.…”

Section: Introductionmentioning

confidence: 99%

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PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Cui

Chen

2021

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) can obtain fully polarimetric information, which provides chances to better understand target scattering mechanisms. Ship detection is an important application of PolSAR and a number of scattering mechanism-based ship detection approaches have been established. However, the backscattering of manmade targets including ships is sensitive to the relative geometry between target orientation and radar line of sight, which makes ship detection still challenging. This work aims at mitigating this issue by target scattering diversity mining and utilization in polarimetric rotation domain with the interpretation tools of polarimetric coherence and correlation pattern techniques. The core idea is to find an optimal combination of polarimetric rotation domain features which shows the best potential to discriminate ship target and sea clutter pixel candidates. With the Relief method, six polarimetric rotation domain features derived from the polarimetric coherence and correlation patterns are selected. Then, a novel ship detection method is developed thereafter with these optimal features and the support vector machine (SVM) classifier. The underlying physics is that ship detection is equivalent to ship and sea clutter classification after the ocean and land partition. Four kinds of spaceborne PolSAR datasets from Radarsat-2 and GF-3 are used for comparison experiments. The superiority of the proposed detection methodology is clearly demonstrated. The proposed method achieves the highest figure of merit (FoM) of 99.26% and 100% for two Radarsat-2 datasets, and of 95.45% and 99.96% for two GF-3 datasets. Specially, the proposed method shows better performance to detect inshore dense ships and reserve the ship structure.

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Section: Resultsmentioning

confidence: 99%

Section: Polarimetric Correlation Patternmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Cui

Chen

2021

Remote Sensing

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“…However, the calculation of the threshold value is usually affected due to the complexity of the background clutter. For polarimetric SAR image, some methods based on polarimetric decomposition have been proposed and exploit the difference in polarimetric scattering characteristics between the vessel target and the background clutter [22]- [25]. The polarimetric decomposition is used to find the polarimetric features that have a strong differentiation between background and vessels to complete the detection.…”

mentioning

confidence: 99%

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Zhou

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized and the sea-land segmentation process to remove land false alarms is time-consuming. Regarding these problems, firstly, a single low-level path aggregation network (S-LPAN) is designed specifically for small target. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Secondly, adaptive dual-polarimetric feature fusion (ADPFF) is proposed to filter the multi-channel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Thirdly, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset (P-DSD) containing small vessel detection and sea-land segmentation labels is created with reference to the LS-SSDD v1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.

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Compact-Pol SAR Urban Area Extraction with Extended Polarimetric Correlation Pattern

Zhang

Chen

2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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PolSAR Ship Detection Based on Polarimetric Correlation Pattern

Cited by 26 publications

References 19 publications

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion and Sea–Land Segmentation in SAR Images

Compact-Pol SAR Urban Area Extraction with Extended Polarimetric Correlation Pattern

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