PolSAR Target Detection via Reflection Symmetry and a Wishart Classifier

Gu, Mingfei; Liu, Hongwei; Wang, Yan; Yang, Dongwen

doi:10.1109/access.2020.2999472

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…The convolutional kernels are selected from the most important regions of the PolSAR image. Difference of Gaussian function 31 is used to determine the important regions. The extracted feature maps have high dimensionality which may contain redundant information.…”

Section: Proposed Classification Methodsmentioning

confidence: 99%

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Imani

2022

Sci Rep

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Polarimetric synthetic aperture radar (PolSAR) image classification is a hot topic in remote sensing field. Although recently many deep learning methods such as convolutional based networks have provided great success in PolSAR image classification, but they need a high volume of labeled samples, which are not usually available in practice, or they cause a high computational burden for implementation. In this work, instead of spending cost for network training, the inherent nature of PolSAR image is used for generation of convolutional kernels for extraction of deep and robust features. Moreover, extraction of diverse scattering characteristics contained in the coherency matrix of PolSAR and fusion of their output classification results with a high confidence have high impact in providing a reliable classification map. The introduced method called discriminative features based high confidence classification (DFC) utilizes several approaches to deal with difficulties of PolSAR image classification. It uses a multi-view analysis to generate diverse classification maps with different information. It extracts deep polarimetric-spatial features, consistent and robust with respect to the original PolSAR data, by applying several pre-determined convolutional filters selected from the important regions of image. Convolutional kernels are fixed without requirement to be learned. The important regions are determined with selecting the key points of image. In addition, a two-step discriminant analysis method is proposed to reduce dimensionality and result in a feature space with minimum overlapping and maximum class separability. Eventually, a high confidence decision fusion is implemented to find the final classification map. Impact of multi-view analysis, selection of important regions as fixed convolutional kernels, two-step discriminant analysis and high confidence decision fusion are individually assessed on three real PolSAR images in different sizes of training sets. For example, the proposed method achieves 96.40% and 98.72% overall classification accuracy by using 10 and 100 training samples per class, respectively in L-band Flevoland image acquired by AIRSAR. Generally, the experiments show high efficiency of DFC compared to several state-of-the-art methods especially for small sample size situations.

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Section: Proposed Classification Methodsmentioning

confidence: 99%

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Imani

2022

Sci Rep

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“…Other detectors, such as the following are also studied for target detection in PolSAR detection: CFAR detector [35], SIFT-like keypoint detector [36], Wishart classifier [37], and deep learning networks [38][39][40], etc. Specifically, the clutter truncation scheme was extended to the CFAR detector for PolSAR imagery [35], providing a truncated version of the Gamma and Wishart model for PWF samples.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

Zhou

2023

Electronics

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Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.

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“…Since the polarimetric scattering mechanism difference between ship and sea clutter is beneficial to ship detection, ship detection using polarimetric SAR (PolSAR) data has become a promising research area for maritime surveillance. Many ship detectors based on PolSAR data have been proposed [1][2][3][4][5][6][7][8][9][10][11][12]. According to whether spatial information is used, PolSAR ship detection methods can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [11], we proposed a PolSAR ship detection method which was based on the physical behavior and spatial characteristics of ship and sea clutter. Reflection symmetry is a useful physical behavior for man-made target detection.…”

Section: Introductionmentioning

confidence: 99%

“…Reflection symmetry is a useful physical behavior for man-made target detection. Man-made targets usually have a reflection asymmetry characteristic and appears as bright blob-like structures on amplitude images of the C 12 and C 23 terms in the polarimetric covariance matrix [11], while sea clutter usually has a reflection symmetry characteristic and appears as dark regions on the above images. Thus, ships can be detected by detecting these bright blob-like structures.…”

Section: Introductionmentioning

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

Self Cite

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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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PolSAR Target Detection via Reflection Symmetry and a Wishart Classifier

Cited by 6 publications

References 30 publications

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Two-step discriminant analysis based multi-view polarimetric SAR image classification with high confidence

Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter

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

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