A new scattering similarity based metric for ship detection in Pol-SAR image

Tao, Yunhong; Lang, Haitao; He, Shi

doi:10.1109/igarss.2018.8651433

Cited by 1 publication

(1 citation statement)

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“…Nonparametric methodologies can model arbitrary probability density functions with general assumptions and a certain approximation error [33]. As a nonparametric method, kernel density estimation (KDE) can produce smooth continuous differentiable functions and ensure a good probability density function approximation for a set of given data [34,35]. KDE has been used in many applications.…”

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

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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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%