Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model
AbstractA few previous studies have illustrated the potentials of compact polarimetric Synthetic Aperture Radar (CP SAR) in ship detection. In this paper, we design a ship detection algorithm of CP SAR from the perspective of computer vision. A ship detection algorithm using the pulsed cosine transform (PCT) visual attention model is proposed to suppress background clutter and highlight conspicuous ship targets. It is the first time that a visual attention model is introduced to CP SAR application. The proposed algorithm is a quick and complete framework for practical use. Polarimetric features—the relative phase δ and volume scattering component—are extracted from m-δ decomposition to eliminate false alarms and modify the PCT model. The constant false alarm rate (CFAR) algorithm based on lognormal distribution is adopted to detect ship targets, after a clutter distribution fitting procedure of the modified saliency map. The proposed method is then tested on three simulated circular-transmit-linear-receive (CTLR) mode images, which covering East Sea of China. Compared with the detection results of SPAN and the saliency map with only single-channel amplitude, the proposed method achieves the highest detection rates and the lowest misidentification rate and highest figure of merit, proving the effectiveness of polarimetric information of compact polarimetric SAR ship detection and the enhancement from the visual attention model. View Full-Text
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Xu, L.; Zhang, H.; Wang, C.; Zhang, B.; Tian, S. Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model. Remote Sens. 2016, 8, 751.
Xu L, Zhang H, Wang C, Zhang B, Tian S. Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model. Remote Sensing. 2016; 8(9):751.Chicago/Turabian Style
Xu, Lu; Zhang, Hong; Wang, Chao; Zhang, Bo; Tian, Sirui. 2016. "Compact Polarimetric SAR Ship Detection with m-δ Decomposition Using Visual Attention Model." Remote Sens. 8, no. 9: 751.
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