PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Li, Haoliang; Cui, Xing-Chao; Chen, Siwei

doi:10.3390/rs13193932

Cited by 19 publications

(11 citation statements)

References 33 publications

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“…The similarity metric between pixels is defined based on the symmetrized Kullback-Leibler divergence [32] of polarimetric covariance matrixes as introduced in [10], after comparing the detection performance of P vi computed using different distance metrics on synthetic data. The expression of the symmetrized Kullback-Leibler divergence is as follows [18]:…”

Section: Ship Detection Based On the Keypoint And Patch Variation Ind...mentioning

confidence: 99%

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PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

Gu¹,

Wang²,

Liu³

et al. 2022

Remote Sensing

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The detection of ships on the open sea is an important issue for both military and civilian fields. As an active microwave imaging sensor, synthetic aperture radar (SAR) is a useful device in marine supervision. To extract small and weak ships precisely in the marine areas, polarimetric synthetic aperture radar (PolSAR) data have been used more and more widely. We propose a new PolSAR ship detection method which is based on a keypoint detector, referred to as a PolSAR-SIFT keypoint detector, and a patch variation indicator in this paper. The PolSAR-SIFT keypoint detector proposed in this paper is inspired by the SAR-SIFT keypoint detector. We improve the gradient definition in the SAR-SIFT keypoint detector to adapt to the properties of PolSAR data by defining a new gradient based on the distance measurement of polarimetric covariance matrices. We present the application of PolSAR-SIFT keypoint detector to the detection of ship targets in PolSAR data by combining the PolSAR-SIFT keypoint detector with the patch variation indicator we proposed before. The keypoints extracted by the PolSAR-SIFT keypoint detector are usually located in regions with corner structures, which are likely to be ship regions. Then, the patch variation indicator is used to characterize the context information of the extracted keypoints, and the keypoints located on the sea area are filtered out by setting a constant false alarm rate threshold for the patch variation indicator. Finally, a patch centered on each filtered keypoint is selected. Then, the detection statistics in the patch are calculated. The detection statistics are binarized according to the local threshold set by the detection statistic value of the keypoint to complete the ship detection. Experiments on three data sets obtained from the RADARSAT-2 and AIRSAR quad-polarization data demonstrate that the proposed detector is effective for ship detection.

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Section: Ship Detection Based On the Keypoint And Patch Variation Ind...mentioning

confidence: 99%

“…Cui et al proposed a PolSAR ship detection method based on the polarimetric rotation domain feature [17]. Li et al investigated the optimal combination of the polarimetric rotation domain feature and proposed a ship detection method based on SVM [18].…”

Section: Introductionmentioning

confidence: 99%

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

Gu¹,

Wang²,

Liu³

et al. 2022

Remote Sensing

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“…Recently, in RET detection, many methods exploiting the features of clutter have been developed [25][26][27][28]. In these feature-domain detection methods, features are extracted from range cells as detection statistics, and then targets and clutter are distinguished in the feature space.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

et al. 2023

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In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

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“…The majority of the above approaches generally rely on only intensity information of each polarization channel of single PolSAR data for similar pixel selection. It is known that the polarimetric phase and cross-correlation between two channels contain rich information for similar pixel characterization [27][28][29][30][31]. The neglect of these valuable sources may lead to inaccurate selection of similar pixel candidates and may degrade final speckle filtering performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

Cui

et al. 2023

Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) is an important sensor for earth observation. Multi-temporal PolSAR images obtained by successive observations of the region of interest contain rich polarimetric–temporal–spatial information of the land covers, which has wide applications. Speckle filtering becomes a necessary pre-processing for many subsequent applications. Currently, it is common to filter multi-temporal PolSAR data by directly using a speckle filter developed for single SAR or PolSAR data. The cross-correlation between different time series contains rich information in multi-temporal PolSAR images. How to utilize complete polarimetric–temporal–spatial information becomes a large challenge to achieve more satisfied performances of speckle reduction and details preservation simultaneously. This work dedicates to this issue and develops a novel speckle filtering approach for multi-temporal PolSAR data by multi-dimensional information fusion. The core idea is to establish an adaptive and efficient strategy of similar pixel selection based on the similarity test of multi-temporal polarimetric covariance matrices. This similar pixel selection scheme fuses the complete information of multi-temporal PolSAR data. The sensitivity of the proposed scheme is demonstrated with several typical and challenging texture patterns. Then, an adaptive speckle filter is established specifically for multi-temporal PolSAR data. Intensive comparison studies are carried out with airborne UAVSAR datasets and spaceborne ALOS/PALSAR datasets. Quantitative investigations in terms of the equivalent number of looks (ENL) and the figure of merit (FOM) indexes demonstrate and validate the superiority of the proposed method.

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PolSAR Ship Detection with Optimal Polarimetric Rotation Domain Features and SVM

Cited by 19 publications

References 33 publications

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

PolSAR Ship Detection Based on a SIFT-like PolSAR Keypoint Detector

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

Adaptive Speckle Filter for Multi-Temporal PolSAR Image with Multi-Dimensional Information Fusion

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